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DOE OSTI2019

Solar Resource Calibration, Measurement, and Dissemination: Final Report FY 2016-FY 2018

The Solar Resource Calibration, Measurement, and Dissemination project supports the U.S. Department of Energy's Solar Energy Technologies Office (SETO) initiative to make solar energy cost-competitive with other forms of electricity by improving the tools and methods to measure and model solar radiation and therefore reduce uncertainty in predicting solar output and improve the bankability of solar projects. This project has three tasks that conduct research on advancing solar resource measurements and modeling: Task 1: Applied solar Radiation Measurements. Task 2: National Solar Radiation Data Base (NSRDB). Task 3: Knowledge Sharing. The National Renewable Energy Laboratory's (NREL's) Solar Radiation Research Laboratory (SRRL) activities ensure traceable solar measurements throughout the United States that are essential for project feasibility, due diligence, financing, and plant operations. The SRRL houses NREL's Baseline Measurement System (BMS), which provides a platform for improving the accuracy of solar measurements through research to improve the calibration and characterization of instruments and enable the development of new measurement technology and standards. The platform contains a variety of instruments measuring solar radiation and other meteorological parameters. Following best practices, BMS instruments are calibrated to ensure traceability to the World Radiometric Reference in compliance with ISO/IEC 17025 accreditation requirements. The BMS data sets are quality-controlled and made publicly available through the Measurement and Instrumentation Data Center website. The NSRDB is the most accessed public database of solar irradiance and associated weather parameters for use in energy modeling. It supports SETO's goals of reducing barriers to high penetrations of solar technologies by providing easy access to high-quality, foundational data essential for innovative product development and downstream modeling through the NSRDB website. Data are currently available for the contiguous United States and beyond for 20 years covering 1998-2017. The Physical Solar Model (PSM) that underpins the current NSRDB data sets is a unique physics-based model that has been developed at NREL and has opened the door to the use of next-generation satellite data sets for solar resource assessment and forecasting. Through the American Society for Testing Materials (ASTM) International and other international standards organizations, NREL continues to provide leadership in the development of standards that are relevant to the measurement and modeling of solar radiation for solar energy applications. Standards developed cover the calibration method of pyrheliometers (direct normal irradiance measurements), pyranometers (global horizontal irradiance measurements), spectroradiometers, ultraviolet radiometers, correct field deployment of these instruments, spectral distribution reference tables for photovoltaic modeling, and transfer of indoor calibrations. Further, NREL leads the International Energy Agency (IEA) Photovoltaic Power Systems Programme Task 16: Subtask 1, which focuses on the evaluation of current and emerging resource assessment methodologies. NREL and the IEA updated the Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications, which is widely used by the solar industry.

14 SOLAR ENERGY↗

DOE OSTI2023

Intra-hour Solar Irradiance Forecast in Multiple Locations using Deep Transfer Learning

In recent years, solar power system installation imposes several challenges on the operations of local and regional power grids due to the inherent variability of ground-level solar irradiance. This work proposes a novel real-time solar forecast methodology for intra-hour solar irradiance based on deep transfer learning from ground-based sky imager for time horizons ranging from 5-15 min. There are three unique aspects of the proposed methodology: (1) a Deep Learning based algorithm development which is modeled as a classification approach rather than a traditional regression approach; (2) the use of the Transfer Learning technique to show generalization capability, robustness, and portability of baseline model in the newly deployed location where availability of enough data for training is typically scarce, and (3) redefinition of point-based irradiation forecast error estimation technique with a window-based one that is more intuitive and user-friendly. The system is developed using multiple years of irradiance and sky image recording in New Jersey and one-year data from Colorado, USA. The method is validated against ground telemetry from these two locations of diverse geographic and climatic conditions. Results show that the forecasting method proposed in this work is robust and highly accurate (8% MAPE error) for multiple locations deployment.

Deep Learning, Convolution Neural Networks, transf↗

DOE OSTI2014

Feasibility Study on the Use of On-line Multivariate Statistical Process Control for Safeguards Applications in Natural Uranium Conversion Plants

The objective of this work was to determine the feasibility of using on-line multivariate statistical process control (MSPC) for safeguards applications in natural uranium conversion plants. Multivariate statistical process control is commonly used throughout industry for the detection of faults. For safeguards applications in uranium conversion plants, faults could include the diversion of intermediate products such as uranium dioxide, uranium tetrafluoride, and uranium hexafluoride. This study was limited to a 100 metric ton of uranium (MTU) per year natural uranium conversion plant (NUCP) using the wet solvent extraction method for the purification of uranium ore concentrate. A key component in the multivariate statistical methodology is the Principal Component Analysis (PCA) approach for the analysis of data, development of the base case model, and evaluation of future operations. The PCA approach was implemented through the use of singular value decomposition of the data matrix where the data matrix represents normal operation of the plant. Component mole balances were used to model each of the process units in the NUCP. However, this approach could be applied to any data set. The monitoring framework developed in this research could be used to determine whether or not a diversion of material has occurred at an NUCP as part of an International Atomic Energy Agency (IAEA) safeguards system. This approach can be used to identify the key monitoring locations, as well as locations where monitoring is unimportant. Detection limits at the key monitoring locations can also be established using this technique. Several faulty scenarios were developed to test the monitoring framework after the base case or normal operating conditions of the PCA model were established. In all of the scenarios, the monitoring framework was able to detect the fault. Overall this study was successful at meeting the stated objective.

Ladd-Lively, Jennifer L↗

NASA NTRSDate not supplied

System Safety Analysis of Complex NASA Systems with Model-Based Engineering (REV B)

The emergence of model-based engineering is transforming design and analysis methodologies. A recognized benefit of model-based engineering is the existence of a “single source of truth” about the system that becomes the authoritative source of data and information for designers, analysts, and developers. This promotes consistency and efficiency as the design emerges and can be used to further optimize the design. Integrating System Safety Engineers to the “single source of truth” will ensure that the outputs of their assessments and analyses are relevant to the design as it evolves. Use of an integrated system model enables near immediate evaluation of a design change as well as development of operational processes for risk assessment and communication. Such models can enable efficient and timely analysis of system hazards (e.g., hazard fault tree analysis and procedure simulations) and produce complete, accurate, and more consistent products (e.g., hazard reports and safety requirement evaluations). Therefore, an agency-sponsored team at Goddard Space Flight Center (GSFC) recently completed a System Safety Study of modeling and testing capabilities as part of a Model-Based Safety and Mission Assurance Initiative (MBSMAI). Using an existing model developed for reliability analyses, GSFC modeling and system safety experts performed system safety analysis/modeling and produced safety products. The team evaluated model-based feasibility to support System Safety Engineering, developed safety analysis modeling processes, and identified tool capability advancement/development needs. These study results indicate model-based engineering is valid and useable for System Safety Engineering for NASA if adequate modeling processes and environment are established.

NASA↗

DOE OSTI2019

Data-enabled structure–property mappings for lanthanide-activated inorganic scintillators

The use of machine learning (ML) models toward development of validated structure–property relationships for two fundamental properties of activated inorganic scintillators for high energy radiation detection, namely the light yield (LY) and the decay time constant, is explored. The ML models are built on easily accessible proxies of materials—interchangeably referred to as features, descriptors or fingerprints—that are carefully selected on the basis of a physical understanding of the scintillation mechanism. Our study indicates that the developed physics-based ML models employing kernel ridge regression (KRR) and AdaBoost algorithm applied on top of a decision tree-based regression are able to “learn” the underlying design rules in a multi-dimensional feature space and thereby enable reasonably accurate predictions of the two target properties on unseen compounds (i.e., on a held-out test set). For instance, within a set of twenty-five cerium- or europium-doped scintillator materials, our analysis reveals a strong correlation between the average ionic part of the dielectric constant and the LY, irrespective of the specific chemistry of the compounds, indicating that the average ionic part of the dielectric constant is a particularly relevant descriptor toward prediction of the LY. Our results also demonstrate that, despite the use of small training datasets, the developed models are able to quickly distinguish high performing chemistries from those with relatively poor performance and therefore can play a crucial role in screening of new compounds with an attractive combination of targeted properties. Finally, the present study provides necessary motivation for future efforts involving ML models with relatively large training datasets, vast feature space explorations, and experimental design in search of promising novel scintillator chemistries.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Coarse woody debris decomposition assessment tool: Model development and sensitivity analysis

Coarse woody debris (CWD) is an important component in forests, hosting a variety of organisms that have critical roles in nutrient cycling and carbon (C) storage. We developed a process-based model using literature, field observations, and expert knowledge to assess woody debris decomposition in forests and the movement of wood C into the soil and atmosphere. The sensitivity analysis was conducted against the primary ecological drivers (wood properties and ambient conditions) used as model inputs. The analysis used eighty-nine climate datasets from North America, from tropical (14.2° N) to boreal (65.0° N) zones, with large ranges in annual mean temperature (26.5°C in tropical to -11.8°C in boreal), annual precipitation (6,143 to 181 mm), annual snowfall (0 to 612 kg m -2 ), and altitude (3 to 2,824 m above mean see level). The sensitivity analysis showed that CWD decomposition was strongly affected by climate, geographical location and altitude, which together regulate the activity of both microbial and invertebrate wood-decomposers. CWD decomposition rate increased with increments in temperature and precipitation, but decreased with increases in latitude and altitude. CWD decomposition was also sensitive to wood size, density, position (standing vs downed), and tree species. The sensitivity analysis showed that fungi are the most important decomposers of woody debris, accounting for over 50% mass loss in nearly all climatic zones in North America. The model includes invertebrate decomposers, focusing mostly on termites, which can have an important role in CWD decomposition in tropical and some subtropical regions. The role of termites in woody debris decomposition varied widely, between 0 and 40%, from temperate areas to tropical regions. Woody debris decomposition rates simulated for eighty-nine locations in North America were within the published range of woody debris decomposition rates for regions in northern hemisphere from 1.6° N to 68.3° N and in Australia.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Reliability Analysis of Power Grids Considering Component Failures of Variable Energy Resources

This paper proposes an improved model for the reliability assessment of power systems considering component failures of variable energy resources (VER). The inherent intermittency of VER such as solar photovoltaic (PV) and wind farms, along with their susceptibility to component failures, present significant challenges to reliable system operation. These issues, combined with power grid operation and network constraints, complicate the reliable operation of VER-integrated power systems. Here, to address these concerns, this paper introduces a reliability assessment framework that considers VER input variability, its impact on component availability, and their resulting impact on overall system reliability. Stochastic models based on discrete Markov processes are developed to incorporate variable irradiance, wind speeds, and their effects on PV and wind component failure rates. A next-event and state transition-based approach is then developed to integrate the stochastic models into a mixed-timing sequential Monte Carlo simulation framework for composite reliability assessment. Case studies on the RTS-GMLC system demonstrate the effectiveness of the proposed model in evaluating the reliability of VER-integrated systems.

Pandit, Dilip [Sandia National Laboratories (SNL-N↗

DOE OSTI2016

Understanding GPU Power. A Survey of Profiling, Modeling, and Simulation Methods

Modern graphics processing units (GPUs) have complex architectures that admit exceptional performance and energy efficiency for high throughput applications.Though GPUs consume large amounts of power, their use for high throughput applications facilitate state-of-the-art energy efficiency and performance. Consequently, continued development relies on understanding their power consumption. Our work is a survey of GPU power modeling and profiling methods with increased detail on noteworthy efforts. Moreover, as direct measurement of GPU power is necessary for model evaluation and parameter initiation, internal and external power sensors are discussed. Hardware counters, which are low-level tallies of hardware events, share strong correlation to power use and performance. Statistical correlation between power and performance counters has yielded worthwhile GPU power models, yet the complexity inherent to GPU architectures presents new hurdles for power modeling. Developments and challenges of counter-based GPU power modeling is discussed. Often building on the counter-based models, research efforts for GPU power simulation, which make power predictions from input code and hardware knowledge, provide opportunities for optimization in programming or architectural design. Noteworthy strides in power simulations for GPUs are included along with their performance or functional simulator counterparts when appropriate. Lastly, possible directions for future research are discussed.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2017

Final Report: Utilizing Alternative Fuel Ignition Properties to Improve SI and CI Engine Efficiency

Experimental and modeling studies were completed to explore leveraging physical and chemical fuel properties for improved thermal efficiency of internal combustion engines. Fundamental studies of the ignition chemistry of ethanol and iso-octane blends and constant volume spray chamber studies of gasoline and diesel sprays supported the core research effort which used several reciprocating engine platforms. Single cylinder spark ignition (SI) engine studies were carried out to characterize the impact of ethanol/gasoline, syngas (H 2 and CO)/gasoline and other oxygenate/gasoline blends on engine performance. The results of the single-cylinder engine experiments and other data from the literature were used to train a GT Power model and to develop a knock criteria based on reaction chemistry. The models were used to interpret the experimental results and project future performance. Studies were also carried out using a state of the art, direct injection (DI) turbocharged multi- cylinder engine with piezo-actuated fuel injectors to demonstrate the promising spray and spark timing strategies from single-cylinder engine studies on the multi-cylinder engine. Key outcomes and conclusions of the studies were: 1. Efficiency benefits of ethanol and gasoline fuel blends were consistent and substantial (e.g. 5-8% absolute improvement in gross indicated thermal efficiency (GITE)). 2. The best ethanol/gasoline blend (based on maximum thermal efficiency) was determined by the engine hardware and limits based on component protection (e.g. peak in-cylinder pressure or maximum turbocharger inlet temperature) – and not by knock limits. Blends with <50% ethanol delivered significant thermal efficiency gains with conventional SI hardware while maintain good safety integrity to the engine hardware. 3. Other compositions of fuel blends including syngas (H 2 and CO) and other dilution strategies provided significant efficiency gains as well (e.g. 5% absolute improvement in ITE). 4. When the combination of engine and fuel system is not knock limited, multiple fuel injection events maintain thermal efficiency while improving engine-out emissions (e.g. CO, UHC, and particulate number).

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2017

Building thermal model development of typical house in U.S. for virtual storage control of aggregated building loads based on limited available information

A building thermal model is an essential component for achieving optimal control of a building’s heating, ventilation, and air conditioning (HVAC). A self-learning grey-box model has been developed by implementing data-driven techniques and utilizing limited available information about the building. In previous research, detailed foreknown knowledge/information, e.g. physical and thermal properties of building materials, as well as sufficient number of observation points, e.g. indoor temperature sensors in different zones, are available for making the decision of model structure and parameters searching range easier. The availability of measured data and a narrowed searching range of model parameters, e.g. resistance and capacity for each wall, made the adopted algorithms quickly achieve near optimal values, which closely approximate the actual heat transfer of a building. Meanwhile, the details of pre-processing of the key inputs were seldom explained. Compared to the previous research, in this study, the available information is assumed to be limited which can fill the requirement for large scale virtual storage control of real residential community in near future, where it may be impractically or over time-consuming to acquire the detailed information for each single house. The developed model is validated by comparing the results to measured data in a recently-built home typical of the southeastern United States.

Cui, Borui↗

DOE OSTI2020

Heat Integration Optimization and Dynamic Modeling Investigation for Advancing the Coal-Direct Chemical Looping Process

The purpose of the project is to address the optimization and startup operation of a modular coal direct chemical looping (CDCL) combustion system integrated with a steam cycle for power generation to reduce the risks involved in further scale-up of the technology. The modular reactor design of the CDCL process provides flexibility in the fabrication of the reactor and in its operating capacity (i.e. turndown ratio) at the cost of a more complex heat exchange network (HEN) design and integration. To address the technology gaps and advance the efficiency and economic feasibility of the CDCL technology, the project will perform a detailed and comprehensive analysis of the integration of a modular CDCL reactor system and a steam cycle system under both static and transient conditions via HEN process performance simulations and system dynamic modeling, respectively. The scope of work consists of 1) Experimental and computational studies of the CDCL combustor reactor 2) Comprehensive static (i.e. steady-state) system HEN design analysis in CDCL 550 MWe commercial unit for power generation and 3) Dynamic modeling of site specific design of 10MWe CDCL large pilot plant. The project team has successfully developed and validated a kinetic model for the oxidation of oxygen carriers in the combustor using the unreacted shrinking core model (UCSM). The model is capable of capturing the oxidation kinetics of fully or partially reduced oxygen carrier particles. A computational fluid dynamics (CFD) model is developed to simulate the hydrodynamics, heat transfer, and chemical reaction occurring in the CDCL combustor. The model is developed in MFIX and ANSYS Fluent. Key aspects of CDCL combustor operation, including heat transfer, oxygen carrier oxidation, and the transport of oxygen carrier particles, are simulated using this CFD model. The HEN for a commercial scale 550 MWe CDCL power plant is simulated and optimized using ASPEN Plus. Practical design considerations are incorporated based on industrial experiences. The performance and cost for the commercial CDCL plant is updated based on these analyses. A dynamic model for the 10 MWe CDCL pilot plant is developed in ProTRAX simulation software. The model is based on the pilot plant design developed in project DE-FE0027654 “10 MWe CDCL Large Pilot Plang – Pre-FEED Study” and the steam cycle data obtained from Dover Light & Power plant. The transient behaviors during pilot plant load variation are simulated using the dynamic model.

01 COAL, LIGNITE, AND PEAT↗

NASA NTRS1986

An interplanetary magnetic field dependent model of the ionospheric convection electric field

An IMF-dependent model of the magnetospheric electric field at ionospheric altitudes has been developed based on published observations, qualitative models, and a limited understanding of the electric field source. The empirical inputs are discussed, and the model is presented in an ionospheric convection situation where corotation is an important ingredient. This leads to a description of sunward ionospheric plasma transport in the polar cap for northward IMF orientations. The validity of the model is discussed, and areas in which more empirical results are required are specified.

Sojka, J. J.↗

NASA NTRSDate not supplied

A New BRDF Hotspot Model and its Use in Improving Radiative Transfer Model Efficiency and Application to VLIDORT-based PCRTM Model

The term “hotspot” refers to the sharp increase of reflectance occurring when incident (solar) and reflected (viewing) directions almost coincide in the backscatter direction. The accurate simulation of hotspot directional signatures is important for many remote sensing applications. The RossThick-LiSparse-Reciprocal (RTLSR) Bidirectional Reflectance Distribution Function (BRDF) model is widely used in radiative transfer simulations, and the hotspot model mostly used is from Maignan- Bréon but it typically requires large values of numerical quadrature and Fourier expansion terms in order to represent the hotspot accurately. To improve its use in atmospheric radiative transfer (RT) model simulations, we have developed a modified version based on the Maignan-Bréon’s hotspot BRDF model that converge much faster numerically, making it more practical for use in RT models that require Fourier expansion of BRDF to simulate the top-of-atmosphere (TOA) hotspot signatures. On basis of the vector linearized discrete ordinate radiative transfer model (VLIDORT), we have built a line-by-line based simulation system that can simulate the TOA radiance for atmospheres with 26 gases, different aerosols, dust and clouds, and surface with different BRDF. Using simulated results under diverse atmospheric conditions, we have developed VLIDORT-PCRTM model.

Xiaozhen Xiong↗

NASA NTRS1996

Microstructural Development during Directional Solidification of Peritectic Alloys

A thorough understanding of the microstructures produced through solidification in peritectic systems has yet to be achieved, even though a large number of industrially and scientifically significant materials are in this class. One type of microstructure frequently observed during directional solidification consists of alternating layers of primary solid and peritectic solid oriented perpendicular to the growth direction. This layer formation is usually reported for alloy compositions within the two-phase region of the peritectic isotherm and for temperature gradient and growth rate conditions that result in a planar solid-liquid interface. Layered growth in peritectic alloys has not previously been characterized on a quantitative basis, nor has a mechanism for its formation been verified. The mechanisms that have been proposed for layer formation can be categorized as either extrinsic or intrinsic to the alloy system. The extrinsic mechanisms rely on externally induced perturbations to the system for layer formation, such as temperature oscillations, growth velocity variations, or vibrations. The intrinsic mechanisms approach layer formation as an alternative type of two phase growth that is inherent for certain peritectic systems and solidification conditions. Convective mixing of the liquid is an additional variable which can strongly influence the development and appearance of layers due to the requisite slow growth rate. The first quantitative description of layer formation is a model recently developed by Trivedi based on the intrinsic mechanism of cyclic accumulation and depiction of solute in the liquid ahead of the interface, linked to repeated nucleation events in the absence of convection. The objective of this research is to characterize the layered microstructures developed during ground-based experiments in which external influences have been minimized as much as possible and to compare these results to the current the model. Also, the differences between intrinsic and externally influenced layer formation were explored. The choice of alloy system is critical to a study of the formation of layered microstructures. The ideal system would have a well-characterized phase diagram, equal densities of both elements in the liquid state to minimize compositionally-driven convective flows, a low peritectic temperature to simplify directional solidification and the achievement of a high temperature gradient in the liquid, a broad composition range for the peritectic reaction, and a reasonable hardness at room temperature to facilitate handling and metallographic preparation. The In-Sn system was selected initially due to a very low peritectic temperature and the nearly equal densities of In and Sn in the liquid state. Since the In-rich peritectic reaction had apparently not been utilized previously for solidification research, experiments were conducted to check the phase diagram in the region of interest. The alloys in this system proved to be difficult to handle and prepare in bulk form with the equipment available, so experiments were initiated with the Sn-Cd system. Layered microstructures had been observed previously in Sn-Cd.

Lograsso, Thomas A.↗

NASA NTRS2007

CFD Modeling of Helium Pressurant Effects on Cryogenic Tank Pressure Rise Rates in Normal Gravity

A recently developed computational fluid dynamics modeling capability for cryogenic tanks is used to simulate both self-pressurization from external heating and also depressurization from thermodynamic vent operation. Axisymmetric models using a modified version of the commercially available FLOW-3D software are used to simulate actual physical tests. The models assume an incompressible liquid phase with density that is a function of temperature only. A fully compressible formulation is used for the ullage gas mixture that contains both condensable vapor and a noncondensable gas component. The tests, conducted at the NASA Marshall Space Flight Center, include both liquid hydrogen and nitrogen in tanks with ullage gas mixtures of each liquid's vapor and helium. Pressure and temperature predictions from the model are compared to sensor measurements from the tests and a good agreement is achieved. This further establishes the accuracy of the developed FLOW-3D based modeling approach for cryogenic systems.

Grayson, Gary↗

NASA NTRSDate not supplied

System Safety Analysis of Complex NASA Systems with Model-Based Engineering

The emergence of model-based engineering is transforming design and analysis methodologies [5]. A recognized benefit of model-based engineering is the existence of a “single source of truth” about the system that becomes the authoritative source of data and information for designers, analysts, and developers. This promotes consistency and efficiency as the design emerges and can be used to further optimize the design. Integrating System Safety Engineers to the “single source of truth” will ensure that the outputs of their assessments and analyses are relevant to the design as it evolves. Use of an integrated system model enables near immediate evaluation of a design change as well as development of operational processes for risk assessment and communication. Such models can enable efficient and timely analysis of system hazards (e.g., hazard fault tree analysis and procedure simulations) and produce complete, accurate, and more consistent products (e.g., hazard reports and safety requirement evaluations). Therefore, an agency-sponsored team at Goddard Space Flight Center (GSFC) recently completed a System Safety Study of modeling and testing capabilities as part of a Model-Based Safety and Mission Assurance Initiative (MBSMAI). Using an existing model developed for reliability analyses [1], GSFC modeling and system safety experts performed system safety analysis/modeling and produced safety products. The team evaluated model-based feasibility to support System Safety Engineering, developed safety analysis modeling processes, and identified tool capability advancement/development needs. These study results indicate model-based engineering is valid and useable for System

NASA↗

NASA NTRSDate not supplied

System Safety Analysis of Complex NASA Systems with Model Based Engineering

Model Based Engineering↗

DOE OSTI2018

Structural Health Monitoring of Nuclear Spent Fuel Storage Facilities

Interim storage of spent nuclear fuel from reactor sites has gained additional importance and urgency for resolving waste-management-related technical issues. To ensure that nuclear power remains clean energy, monitoring has been identified by DOE as a high priority cross-cutting need, necessary to determine and predict the degradation state of the systems, structures, and components (SSCs) important to safety (ITS). Therefore, nondestructive structural condition monitoring becomes a need to be installed on existing or to be integrated into future storage system to quantify the state of health or to guarantee the safe operation of nuclear power plants (NPPs) during their extended life span. In this project, the lead university and the collaborating national laboratory teamed to develop a nuclear structural health monitoring (n-SHM) system based on in-situ piezoelectric sensing technologies that can monitor structural degradation and aging for nuclear spent fuel DCSS and similar structures. We also aimed to identify and quantify possible influences of nuclear spent fuel environment (temperature and radiation) to the piezoelectric sensor system and come up with adequate solutions and guidelines therefore. We have therefore developed analytical model for piezoelectric based n-SHM methods, with considerations of temperature and irradiation influence on the model of sensing and algorithms in acoustic emission (AE), guided ultrasonic waves (GUW), and electromechanical impedance spectroscopy (EMIS). On the other side, experimentally the temperature and irradiation influence on the piezoelectric sensors and sensing capabilities were investigated. Both short-term and long-term irradiation investigation with our collaborating national laboratory were performed. Moreover, we developed multi-modal sensing, validated in laboratory setup, and conducted the testing on the We performed multi-modal sensing development, verification and validation tests on very complex structures including a medium-scale vacuum drying chamber and a small-scale mockup canister available for the desired testing. Our work developed the potential candidate for long term structural health monitoring of spent fuel canister through piezoelectric wafer sensors and provided the sensing methodologies based on AE and GUW methodologies. It overall provides an innovative system and methodology for enhancing the safe operation of nuclear power plant. All major accomplishments planned in the original proposal were successfully achieved.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗