Search NASA⌕ Search

SEARCH · Search NASA

Results for “Reliability Engineering”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

CIE Analysis Process for Engineered Systems

"CIE Analysis Process for Engineered Systems" outlines a comprehensive methodology for integrating Cyber-Informed Engineering (CIE) principles into both new and existing engineered systems. Sponsored by the U.S. Department of Energy’s Office of Cybersecurity, Energy Security, and Emergency Response (DOE CESER), the process aims to achieve cyber-informed decisions by producing functional security requirements for new systems and retrofitting existing systems to mitigate digital risks. The document details a step-by-step approach, including mission and function definition, digital asset awareness, consequence analysis, and mitigation analysis. It emphasizes the importance of documenting mechanical, electrical, programmable, and network components to protect system functions and provides examples and considerations for each step. The ultimate goal is to ensure that engineered systems remain resilient against cyber threats, maintaining safety, performance, and reliability.

42 - ENGINEERING↗
DOE OSTI2025

Factors controlling injection-induced rupture of intersecting faults during geological sequestration of CO 2

This study addresses coupled multiphase fluid flow and geomechanics effects on potential fault activation associated with subsurface CO 2 injection around intersecting faults. An enhanced fault-representation model is used to capture geomechanical responses of two intersecting faults with finite length during CO 2 injection. The faults are embedded in a strike-slip stress regime of a caprock-reservoir-basement system with the faults represented by zero-thickness interfaces with adjacent finite-thickness damage zones. A sensitivity analysis is conducted to study the effect of fault permeability, slip-weakening behavior, well location relative to the orientation of faults, and well placement (the number and location of injection wells). Five metrics (pressure, CO 2 plume, shear state on the fault, as well as shear displacement and stress path at selected fault monitoring points) are selected to assess CO 2 migration and reactivation of intersecting faults. The results show that induced ruptures are favored by low permeability faults due to high pressure buildup and by slip-weakening behavior resulting from fault strength reduction. The location of one injection well relative to fault orientation determines the magnitude of changes in effective normal stress and shear stress, affecting the location of induced ruptures. Well placement (two injection wells used in the paper) dominates pressure diffusion around the intersection and tips of faults. This redistributes changes in effective normal stress caused by each injection well, influencing the spatial distribution of ruptures along faults. A larger injection volume induces far-field ruptures that are controlled by stress transfer within the injection layer. The findings presented here can provide valuable insights into engineering operations for a long-term, safe, and reliable geologic CO 2 storage.

Fault permeability↗
DOE OSTI2025

Understanding the limitations and potential of micro tensile testing of tungsten and needs for crystal plasticity modelling

Tungsten is the leading plasma facing material candidate due to its exceptional properties. Understanding the response to neutron irradiation is crucial for the lifetime evaluation of tungsten. The limited space in nuclear reactors and the high levels of radioactivity of the specimens after irradiation are significant barriers to accurately measuring the mechanical properties after neutron irradiation. Testing of micro tensile specimens is one approach to reduce the total amount of irradiated material needed for a set of mechanical testing experiments. However, micro samples are not necessarily measuring the bulk material properties, as size effects produce higher measured mechanical properties than is observed in engineering size counterparts. Determining the minimal sample size for reliable bulk property measurement, combined with modeling efforts, is essential. We conducted room temperature tensile tests on tungsten micro tensile specimens fabricated with focused ion beam, plasma focused ion beam and femto-second laser ablation system to dimension of 2×2×7?µm³, 7×7×18?µm³, and 80×100×233?µm³ (width×thickness×gauge-length), respectively. Elevated tensile testing was performed up to 475°C on 5×5×18?µm³ specimens fabricated with focused ion beam. The smallest specimens exhibited a high degree of ductility and strength, whereas the largest specimens demonstrated behavior akin to bulk tungsten. To investigate the effects introduced by the micro specimen fabrication processes and to obtain the necessary bulk dislocation density for the crystal plasticity model, we employed X-ray diffraction depth profiling. This measurement was performed using different X-ray sources with varying penetration depths. Finally, the potential and limitations of micro mechanical tests for tungsten will be discussed.

36 - MATERIALS SCIENCE↗
DOE OSTI2025

Colloidal Behavior of Plutonium Oxide in Concentrated Electrolyte Solutions

The Hanford Site in Washington State manages legacy high-level radioactive waste streams that display major chemistry and engineering challenges, including the high salt levels and pH values that correspond to conditions under which many classical concepts describing chemical reactivity cannot be applied. One particular challenge that needs to be tackled at Hanford is that Pu concentrations, [Pu], in the soluble phases of the tank wastes are higher than expected based on the solubility of crystalline PuO2, which is widely accepted to be caused by the formation of PuO2 colloid, consisting of nano- to submicron-sized particles (PuO2 NPs). Fundamental research underpinning the behavior of PuO2 NPs under conditions not only relevant to the Hanford tank waste but at high ionic strength in general is needed to reliably predict the chemical reactivity of PuO2 NPs and develop engineering solutions to safely and efficiently process high-level radioactive waste into forms suitable for long-term storage. In this work, we study the behavior of PuO2 NPs (particle size ~100 nm) under high ionic strength conditions by reacting it with highly concentrated (up to 5 M) salt solutions. We explore different electrolyte compositions to elucidate the impact of different anions (NO3-, Cl-, ClO4-, SO42-, C2O42-, CO32-) on the stability of PuO2 NP in the acidic and alkaline pH regime. PuO2 NP aggregation and precipitation as function electrolyte concentration is tracked by a combination of liquid scintillation counting, dynamic light scattering for determination of particle size distributions, and zeta potentials as a proxy for particle charge. At acidic pH, electrolytes containing non-coordinating anions, such as NaNO3, NaCl, and NaClO4 mostly stabilize PuO2 NPs over a large electrolyte concentration range, showing only subtle differences in their reactivity. Other electrolyte anions show a more pronounced effect on the PuO2 NP stability: SO42-, binds directly to the particles’ surface, reverses the particle charge, and precipitates the PuO2 NPs efficiently even at intermediate sulfate concentrations (>0.1 M). In contrast, C2O42- is found to lead to high [Pu] in solution, in the milli-molar range, even at mildly acidic pH (~4). Thermodynamic modeling of the dissolved Pu concentrations using PHREEQC is unable to predict the observed [Pu] in the acidic pH regime, supporting the influence of colloids in maintaining elevated [Pu]. It is noteworthy that the current thermodynamic databases do not include constants for colloidal Pu phases and cannot accurately predict many of the high ionic strength solutions relevant to this work. The mechanisms and models responsible for these observations will need further investigation in the future. At high pH values (~12), PuO2 NPs exhibits classical sol-gel chemistry, meaning that upon destabilization of the colloidal sol, for example by addition of concentrated NaOH, highly porous and viscid PuO2 coagulates are formed that consist of a three-dimensional network likely held together by physical interactions. The PuO2 NP coagulate shows no significant reversibility of the aggregation when contacted with concentrated brines; however, PuO2 NPs can be efficiently resuspended in solution by addition of diluted electrolytes, alkaline solutions containing high amounts of carbonate, or simple addition of water. Especially carbonate is shown to stabilize PuO2 NPs in solution at high pH, characterized by stable colloidal suspensions that are resistant against sedimentation during centrifugation. Thermodynamic modeling of the carbonate system was able to predict an increasing dissolved Pu concentration with increasing carbonate concentration. However, the model was profoundly sensitive to the fixed redox potential and does not include any thermodynamic constants for colloidal Pu species.

Neumann, Julia↗
DOE OSTI2024

Heterogeneous Integration Technologies for High-temperature, High-density, Low-profile Power Modules of Wide Bandgap Devices in Electric Drive Applications (Final Technical Report)

The goal of this project is to develop packaging technologies for making high-temperature, high-density, and low-profile wide-bandgap (WBG) power electronics modules for electric drives. These modules are aimed at enabling the DOE’s University Consortium to reach its 2025 inverter targets of ≥ 100 kW/L and ≤ 2.7 $/kW. The specific objectives are to: design and fabricate SiC half-bridge power modules with double-sided cooling and parasitic inductances < 5 nH, heat flux density > 400 W/cm 2 , and working junction temperature of 200 o C; design, fabricate, and deliver a gate driver with double-sided cooled modules for the construction of a 100 kW/L inverter at Oak Ridge National Lab; and design and prototype intelligent gate drivers with integrated current sensor and a low-profile DC-DC power supply with air-core transformer for testing power modules at 200 o C junction temperature. We followed an iterative technical approach of design, simulation, fabrication, and testing of various versions of modules, current sensors, and power supply. The state-of-the-art silicon carbide devices rated at 1.2 kV and 149 A were packaged by sintered-silver bonding on an aluminum nitride direct-bond-copper substrate for high thermal conductivity, high working temperature, and high joint reliability. Porous silver posts were used to interconnect the device’s source pads to the other direct-bond-copper substrate for low mechanical stresses, ease of manufacturing, and double-sided cooling. A current sensor based on package parasitic inductance was developed to measure switching current. A dynamic feedback scheme was developed to compensate the effect of parasitic resistance and temperature variation. A constant-current class-E dc-dc converter with air-core transformer was developed. Air-core transformer was used due to the unavailability of magnetic core at high temperatures. Gate driver and power supply were integrated with the double-side cooled, high temperature SiC power modules for testing the modules at 200 o C junction temperature. Double-pulse and continuous testing of the integrated technologies validated the design and fabrication of the three component technologies. Throughout the project, we overcame the challenge for design verification caused by low prototyping yield, which then helped train the graduate students, the future workforce, to learn the engineering know-how for low-cost manufacturing of reliable products. Below is a summary of the major accomplishments of this project: development of a prototyping process for fabricating double-side cooled (1200 V, 149 A) SiC phase-leg modules capable of working to 200 o C Tj; simulation and experimental verification of the improvement of thermo-mechanical reliability of the double-side cooled SiC phase-leg module by using rigid encapsulant; design and experimental validation of a current sensor based on package parasitic inductance and a compensation solution for eliminating the effect of parasitic resistance; design and experimental validation of a low-profile power supply with six-output air-core transformer for gate driver; functional demonstration of a SiC phase-leg module integrated with its gate driver, current sensor, and an air-core power supply at 200 o C Tj in a double-pulse switching test setup and Buck converter continuous test setup; successful completion of six PhD and two MS students who are or will work at Apple Inc., Tesla Inc., Wolfspeed Inc., Microchip Inc., Monolithic Power Systems Inc., and LG Magna Inc.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Evaluation of desiccation cracking characteristics of inorganic micro-fiber-reinforced engineered barrier material (IMEBM) for geological repository

Abstract Buffer material is crucial for the engineering barrier system to dispose of high-level radioactive waste in a geological repository. A reliable buffer material should be able to maintain good sealing characteristics and minimize desiccation cracking. In this study, the effectiveness of inorganic fiber-reinforced engineering barrier material in reducing desiccation cracks in bentonite was evaluated via desiccation tests, image analysis, and air permeability tests. The effects of fiber type (E-glass fiber and basalt fiber) and fiber content (0.0%, 0.5%, 1.0%, 1.5%, 3.0%, and 5.0% of dry weight of the bentonite) on the development of desiccation cracks in the fiber–bentonite mixtures with the same given initial moisture content were evaluated. The results indicated that the addition of fibers could significantly reduce the crack size and area in bentonite during the drying. Basalt fibers showed a slightly better reinforcement effect than E-glass fibers when the fiber content was lower than 3.0%. The addition of fibers prevented the development of penetrating cracks and significantly reduced the permeability of the bentonite after drying. The permeabilities of basalt fiber- and E-glass fiber-reinforced bentonite composites with 3% reinforcement were 5.81 × 10 –11 m 2 and 7.24 × 10 –11 m 2 , respectively, which were 64 and 51 times smaller than that of pure bentonite. X-ray–CT observation of the internal structure of the samples after drying showed that the addition of fibers significantly changed the crack morphology and potentially increased the tortuosity.

Feng, Yuan↗
DOE OSTI2025

Pushing the limits of NAND technology scaling with ferroelectrics

Artificial intelligence (AI) continues to drive transformative advancements across various industries. The data-intensive nature of AI training (and inferencing) has resulted in the generation of unprecedented volumes of data with machine-generated content surpassing human-generated data by more than 100-fold in 2025. Efficiently managing this data influx necessitates advanced digital storage technologies. However, traditional NAND flash memory, which is critical for supporting data flows in AI systems—alongside high-bandwidth memory, for AI training—faces fundamental scaling limitations as it approaches the 1000-layer milestone, encompassing more than 40 trillion transistors. This article delves into the potential of hafnia-based ferroelectric materials as a breakthrough solution to these challenges. Recent advancements indicate that the intrinsic limitations of ferroelectric field-effect transistors (FEFETs) can be mitigated through material and device-level engineering. These advancements enable FEFETs to meet the stringent density, reliability, and scalability requirements of future three-dimensional NAND technology. The role of ferroelectrics in addressing NAND scaling challenges and expanding storage capabilities presents a promising avenue for meeting the storage demands of the AI-driven era.

3D NAND↗
DOE OSTI1996

Nuclear Safety [Vol. 37, No. 1, January-March 1996]

Nuclear Safety is a journal that covers significant issues in the field of nuclear safety. Its primary scope is safety in the design, construction, operation, and decommissioning of nuclear power reactors worldwide and the research and analysis activities that promote this goal, but it also encompasses the safety aspects of the entire nuclear fuel cycle, including fuel fabrication, spent-fuel processing and handling, and nuclear waste disposal, the handling of fissionable materials and radioisotopes, and the environmental effects of all these activities. Table of Contents for this issue follows. GENERAL SAFETY CONSIDERATIONS: 1 A Decision Support System for Maintenance Management of a Boiling-Water Reactor Power Plant, J. H. Shen, A. Ray, and S. Levine; ACCIDENT ANALYSIS: 12 On Prediction of the Ignition Potential of Uranium Metal and Hydride, M. Epstein, W. Luangdilok, M. G. Plys, and H. K. Fauske; 26 An Overview of the Primary Parameters and Methods for Determining Condensation Heat Transfer to Containment Structures, J. Green and K. Almenas; DESIGN FEATURES: 49 Modem Tornado Design of Nuclear and Other Potentially Hazardous Facilities, J. D. Stevenson and Y. Zaho; U.S. NUCLEAR REGULATORY COMMISSION INFORMATION AND ANALYSES: 73 1994 Accident Sequence Precursor Program Results, R. J. Belles, J. W. Cletcher, D. A. Coplnger, B. W. Dolan, J. W. Minarick, and P. D. O'Reilly; ANNOUNCEMENTS: 93 American Institute of Chemical Engineers (AlChE) Spring 1997 National Meeting; 94 European Safety and Reliability Association International Conference on Safety and Reliability ESREL ’97; 95 Criticality Safety Challenges in the Next Decade; 96 21st International Symposium on the Scientific Basis for Nuclear Waste Management; 84 The Authors; 88 Letter to the Editor; 90 Indexes to Nuclear Safety, Volume 36.

05 NUCLEAR FUELS↗
DOE OSTI1996

Nuclear Safety [Vol. 37, No. 1, January-March 1996]

Nuclear Safety is a journal that covers significant issues in the field of nuclear safety. Its primary scope is safety in the design, construction, operation, and decommissioning of nuclear power reactors worldwide and the research and analysis activities that promote this goal, but it also encompasses the safety aspects of the entire nuclear fuel cycle, including fuel fabrication, spent-fuel processing and handling, and nuclear waste disposal, the handling of fissionable materials and radioisotopes, and the environmental effects of all these activities. Table of Contents for this issue follows. GENERAL SAFETY CONSIDERATIONS: 1 A Decision Support System for Maintenance Management of a Boiling-Water Reactor Power Plant, J. H. Shen, A. Ray, and S. Levine; ACCIDENT ANALYSIS: 12 On Prediction of the Ignition Potential of Uranium Metal and Hydride, M. Epstein, W. Luangdilok, M. G. Plys, and H. K. Fauske; 26 An Overview of the Primary Parameters and Methods for Determining Condensation Heat Transfer to Containment Structures, J. Green and K. Almenas; DESIGN FEATURES: 49 Modem Tornado Design of Nuclear and Other Potentially Hazardous Facilities, J. D. Stevenson and Y. Zaho; U.S. NUCLEAR REGULATORY COMMISSION INFORMATION AND ANALYSES: 73 1994 Accident Sequence Precursor Program Results, R. J. Belles, J. W. Cletcher, D. A. Copinger, B. W. Dolan, J. W. Minarick, and P. D. O'Reilly; ANNOUNCEMENTS: 93 American Institute of Chemical Engineers (AlChE) Spring 1997 National Meeting; 94 European Safety and Reliability Association International Conference on Safety and Reliability ESREL ’97; 95 Criticality Safety Challenges in the Next Decade; 96 21st International Symposium on the Scientific Basis for Nuclear Waste Management; 84 The Authors; 88 Letter to the Editor; 90 Indexes to Nuclear Safety, Volume 36.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

ORNL Design Engineering Library: Validated Thermoelectric Digital Twins

Radioisotope thermoelectric generators (RTGs) serve a crucial role in supplying thermal and electrical energy for reliable and long-duration power in remote and extreme environments. The thermal energy is provided by the decay of radioisotopes and is converted into electrical energy using the steady-state thermoelectric process by exploiting the Seebeck effect. Maintaining a thermal gradient through the material is necessary to drive current production. Modern predictive multi-physics tools are able to effectively describe the complex and strongly coupled phenomena needed for accurate model-based system engineering efforts needed to develop higher performing RTGs. Experimental validation of model simulations is essential to confirm the accuracy, reliability, and applicability of predictive multiphysics tools. Validation fosters confidence among users, helps meet regulatory requirements, and contributes to the ongoing improvement of simulation techniques, ultimately leading to better, safer, and more efficient designs and processes. Validated models can then be used as digital twins and can be interrogated to understand and quantify the performance gaps between theoretical and physical systems. These insights can be used to build higher performing systems.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Micropolar deep material network

This study extends the Deep Material Network (DMN), a physics-informed machine learning framework, to predict the homogenized mechanical response of composite materials with micropolar (Cosserat-type) constitutive behavior. This extension incorporates microstructure-dependent size effects, enabling accurate, efficient, and size-aware predictions for composites with complex internal architectures. While traditional, direct numerical simulation micropolar models effectively capture size effects by introducing extra local degrees of freedom, they bring significant computational challenges, particularly for multiscale analyses relevant to engineering applications. The micropolar DMN developed in this paper achieves high accuracy while significantly reducing computation time compared to micropolar direct numerical simulations. This advancement enables multiscale analyses and parameter studies that were previously impractical, such as high-cycle fatigue simulations and comprehensive investigations of internal length scale effects notably in size-dependent plastic response and the optimization of lattice structures. By uniting microstructure-sensitive modeling, physics-driven learning, and scalable surrogate modeling, the micropolar DMN paves the way for accelerated material design, large-scale parametric studies, and the reliable incorporation of size-dependent effects across a wide range of engineering applications, including optimization and next-generation composite design.

36 MATERIALS SCIENCE↗
DOE OSTI2025

The Sensor Dilemma in Intelligent Transportation Systems: Evaluating Radar, Lidar and Camera: Preprint

Intelligent transportation systems (ITS) are at the forefront in advancing the way we interact with and perceive the transportation network. This revolution is fueled by the significant advancement in sensor perception technologies such as radar, lidar, and video imaging, which are the most popular modalities for ITS. Real-time perception data from these sensors allow intelligent infrastructure-side decision-making to improve the energy, efficiency, and safety at traffic intersections. As traffic departments across the United States transition from traditional loop detectors and emulators and embrace newer technologies, they are often left with a dilemma in choosing a sensor technology for infrastructure-based perception that is reliable, inexpensive, and easy to set up and that has robust performance in varying weather conditions. However, choosing a sensor that checks all these boxes is not straightforward, as every sensor type has unique benefits and drawbacks. Radar is excellent at detecting long-range vehicles and weather resistance but lacks high resolution. Lidar is expensive and weather-sensitive, while cameras provide rich visual data at a low cost but are constrained by lighting and visibility. This study examines radar, lidar, and camera sensor capabilities to ascertain whether any of these qualifies as the "best" sensor for ITS perception. While no single sensor can meet all the demands of ITS, a hybrid approach combining multiple sensor modalities like radar, lidar, and cameras offers the most robust solution for enhancing the safety and efficiency of ITS. Through this evaluation, we hope to draw attention to the necessity of the National Renewable Energy Laboratory's infrastructure perception and control framework, which presents a multisensor track data fusion engine to assimilate multiple data streams in order to provide robust and reliable perception.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Repowering: The Other Side of the Reliability Coin

Extreme weather, cracked backsheets, severe PID, poorly built modules, and installation flaws - all can compromise a solar plant's health and force repowering long before end of life. With more than 70% of U.S. PV capacity less than seven years old, the fleet is young, but its rapid expansion has introduced new materials and system designs that are still being tested under real-world conditions. As a result, reliability - not economics - is what most often drives repowering decisions. Repowering is frequently assumed to be an economically motivated choice, but our work shows that reliability concerns are the real trigger. Drawing from industry interviews, case studies, and modeling, we highlight the physical, electrical, and policy barriers owners face when deciding whether to repair, repower, or decommission. At the same time, repowering can create opportunities: renewed interconnection periods, improved energy yields, and strategic upgrades to extend system value. We present a quantitative framework using NLR's System Advisor Model (SAM) and PV in Circular Economy (PV ICE) tool to evaluate trade-offs across financial, material, and energy impacts. These findings provide practical guidance for navigating the realities of repowering today and underscore the critical role of reliability in shaping the future performance and sustainability of the PV fleet.

14 SOLAR ENERGY↗
DOE OSTI2026

Extending Component Lifetime And Improving Inverter Reliability (ECLAIIR)

Inverter reliability remains one of the most persistent challenges limiting the performance, availability, and economic viability of utility‑scale photovoltaic (PV) plants. Industry data consistently show that inverters account for the highest share of corrective maintenance events and unplanned outages across PV fleets. These failures result in energy losses, increased O&M costs, and reduced confidence in long‑term solar asset performance. Motivated by these challenges, this project—Extending Component Lifetime and Improving Inverter Reliability (ECLAIIR)—was undertaken to systematically investigate inverter degradation and failure mechanisms, develop predictive maintenance capabilities, and establish data‑driven pathways to improve service life and reduce the Levelized Cost of Energy (LCOE) for large‑scale PV systems. The primary goal of the project was to identify pre‑failure signatures in string inverters using both lab‑based accelerated lifetime testing and field‑based data and to develop predictive maintenance algorithms that can anticipate inverter faults before they occur. Through collaboration with inverter testing laboratory, solar PV plant owner, and failure‑analysis experts, the project advanced the technical understanding of inverter reliability. By instrumenting inverters with thermistors, humidity sensors, power‑quality meters, and acoustic sensors, the research established how multiple sensing modalities can reliably detect deviations from normal behavior hours to days before failure. These findings substantially enhance scientific understanding of inverter failure kinetics and provide the PV industry with the most comprehensive cross‑OEM characterization of early‑stage failure indicators reported to date. Technically, the project demonstrated the effectiveness of predictive maintenance by developing and validating the PreDICT (Predictive Diagnostics of PV Inverters Using Condition Monitoring and Trend Analysis) framework—a multi‑layer diagnostic architecture combining peer‑to‑peer analytics, historical trend modeling, and advanced machine‑learning techniques such as the Sequential Conditional Variational Autoencoder (SCVAE). This predictive model achieved more than 90% accuracy in detecting pre‑failure conditions and provided up to four days of lead time before inverter failure in field scenarios. Economically, the project’s LCOE analysis showed that predictive maintenance can reduce lifetime energy losses and minimize corrective maintenance interventions. Modeling indicated that, depending on inverter failure rates and replacement timelines, predictive maintenance can significantly reduce LCOE impacts associated with inverter downtime: from as high as 19.4% under conventional maintenance strategies to 0.1%–10.17% when predictive analytics are adopted. These results confirm that predictive maintenance is both technically feasible and economically advantageous for utilities and plant operators. The project’s findings also have broad public benefit. By improving inverter reliability and reducing downtime, predictive maintenance directly increases electricity generation from existing PV assets. Enhanced reliability lowers operational costs for utilities, which can translate over time into lower energy costs for consumers. Furthermore, the project’s technical publications, conference presentations, and industry workshops ensure that knowledge gained is shared broadly across the solar industry, supporting workforce development and enabling utilities of all sizes to adopt modern asset‑health monitoring practices. The retrofitting case study and service‑life prediction framework further support informed decision‑making for aging PV fleets, helping operators extend system life and reduce electronic waste. In summary, the ECLAIIR project significantly advanced the state of knowledge on inverter degradation, demonstrated the technical and economic value of predictive maintenance, and delivered actionable tools and insights that support more reliable, cost‑effective, and sustainable PV plant operation. The outcomes of this project will continue to inform utility practices, guide inverter design improvements, and strengthen the long‑term performance of solar assets nationwide.

14 SOLAR ENERGY↗
DOE OSTI2025

System Engineers and Decisions: It?s All about Knowledge

In order to guarantee that a system meets adequate levels of reliability and availability, system performances are continuously monitored and analyzed thanks to the technological advancements driving the Industry 4.0 revolution. An Industry 4.0 approach is typically based on advanced statistical, big data mining, machine learning, and internet-of-things methods designed to detect anomalies in the behavior of system, detect the most likely failure modes, and provide indications to system engineers on when maintenance activities should be performed before system performance are deemed unacceptable (which can be generated by diagnostic and prognostic methods). However, these analyses, which are designed to automatize and increase the efficacy of the system maintenance program, require large amount of data which can come in various forms: numeric, textual, images, sounds etc. Such data constitutes the historic knowledge benchmark to track system performances and support system engineer decisions. Here we claim that data is not sufficient to support this kind of analyses when applied to systems characterized by complex architectures and behaviors. Robust system engineer decisions require the ability to understand the system operational context that lies behind the observed data elements. In this respect, system models are in fact necessary to “put data in context” and capture relationships between data elements. Industry 4.0 methods require in fact contextual knowledge as a basis upon which hypotheses can be generated and assumptions tested. In our view, for complex systems, model-based system engineering (MBSE) models can afford this contextual knowledge, as they are typically used to describe systems architecture and dynamic behaviors. System knowledge is here intended as the blending of collected data and system architecture which takes the form of a “knowledge graph”. A knowledge graph is a database which consists of a large set of nodes (in our case an entity can be either a data or an MBSE element) which are linked to each other. The types of nodes and links follow a pre-defined topology, sometimes also refers as an ontology, that is designed to fit the actual decisions that needs to be performed. We show here how a knowledge graph can be defined to support system engineer maintenance decisions and how the same graph can be built based on system MBSE models and pre-processed data from numeric (through anomaly detections and diagnostic methods) and textual elements (through technical language processing TLP).

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2025

The Sensor Dilemma in Intelligent Transportation Systems

Intelligent Transportation Systems (ITS) are at the forefront in advancing the way we interact and perceive with the transportation network. This revolution is fueled by the significant advancement in sensor perception technologies such as Radar, LiDAR and Video Imaging which are the most popular modalities for ITS. Real-time perception data from these sensors allows intelligent infrastructure side decision making to improve the energy, efficiency and safety at traffic intersections. As traffic departments across the United States are transitioning from traditional loop detectors / emulators and embracing newer technologies, they are often left with a dilemma in choosing a sensor technology for infrastructure-based perception which is reliable, inexpensive, easy to setup and has robust performance in varying weather conditions. However, choosing a sensor which checks all boxes is not straightforward as every sensor type has unique benefits and drawbacks. Radar is excellent at detecting long range vehicles and weather resistance but lacks high resolution. LiDAR is expensive and weather-sensitive, while cameras provide rich visual data at a low cost but are constrained by lighting and visibility. This study examines Radar, LiDAR and camera sensors capabilities to ascertain whether any of these qualifies as the "best" sensor for ITS perception. Through this evaluation, we hope to draw attention to the necessity of National Renewable Energy Laboratory's (NREL) Infrastructure Perception and Control (IPC) framework which presents a multi-sensor track data fusion engine to assimilate multiple data streams in order to provide robust and reliable perception. While no single sensor can meet all the demands of ITS, a hybrid approach combining multiple sensor modalities like Radar, LiDAR and cameras, offers the most robust solution for enhancing the safety and efficiency in intelligent transportation systems.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Why Perovskite Thermal Stress is Unaffected by Thin Contact Layers

Metal halide perovskite photovoltaics have emerged as a high efficiency, low-cost alternative that can potentially rival or enhance conventional silicon technology. Despite exceptional initial power conversion efficiencies, achieving compliance with international standards and widespread adoption requires further enhancements to their operational stability. Notably, addressing mechanical strain and stress in brittle perovskites has emerged as a pivotal approach to mitigate chemical degradation and improve reliability during thermal cycling. Here, in this study, a popularized strain engineering strategy is investigated in which a high coefficient of thermal expansion (CTE) hole transport layer (i.e., PDCBT) is cast onto inorganic perovskite (CsPbI 2 Br) at 100 °C. Contrary to previously published results, the X-ray diffraction (XRD):Sin 2 ψ and substrate curvature measurement techniques show that the hole transport layer has no discernible impact on perovskite strain. The accuracy of the XRD:Sin 2 ψ method for measuring strain is highlighted in contrast to an analysis based on shifts of single XRD peaks which can be influenced by multiple artifacts. The findings in this study are in accordance with mechanics theory: thin layers are unable to induce significant strain changes in perovskite thin films as the force they apply is negligible compared to that applied by a thick and stiff substrate.

14 SOLAR ENERGY↗