SEARCH · Search NASA

Results for “Guiding Principles”

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.

SourceResearch connectionFrom yearThrough year

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

At least 757 records · Page 42

All sources NASA NTRS DOE OSTI

NASA NTRSDate not supplied

Leveraging the 2024 Solar Eclipse to Enhance Science Learning through Citizen Science

The solar eclipse on April 8, 2024, provided a rare and compelling opportunity for educators and students to engage in hands-on citizen science. Two NASA Science Activation projects GLOBE Mission Earth (GME) and NASA Earth Science Education Collaborative (NESEC) partnered together to provide a unique professional development experience for educators interested in the eclipse. They invited educators to participate in a specialized 5-week online workshop designed to engage the educators in Global Learning and Observations to Benefit the Environment (GLOBE) citizen science about the eclipse. The workshop was designed to support their certification as GLOBE educators and develop their knowledge, skills, and confidence in conducting a GLOBE investigation. Over 60 educators from across the United States took part in this workshop, which focused on investigating the eclipse by collecting and analyzing atmospheric data. The GLOBE Program promotes environmental and scientific literacy by enabling participants to collect Earth science data and contribute it to a global database accessible for research. The 2024 GLOBE Eclipse workshop trained educators in specific GLOBE protocols—Clouds, Air Temperature, and Surface Temperature—using the GLOBE Eclipse tool integrated into the GLOBE Observer app. This training was supplemented with guidance on engaging students in authentic scientific research and creating research posters to present their findings. The workshop aimed to enhance educators’ skills and confidence in integrating GLOBE protocols into their teaching practices. It addressed several key areas outlined in the National Academies' report on Learning through Citizen Science, including scientific context, nature of participation, and project infrastructure. Educators learned about the atmospheric effects of solar eclipses and were trained to use scientific tools and data analysis methods relevant to their research. They also engaged in live sessions and asynchronous activities, practicing data collection and analysis with real-time feedback. Survey results from the workshop highlighted that participants were primarily motivated by the desire to better use data in their classrooms and improve their proficiency with the GLOBE Observer app. Post-workshop evaluations showed significant increases in educators' confidence regarding their ability to conduct and guide scientific research. Participants reported feeling well-prepared to use the GLOBE Observer app for data collection and were successful in integrating their eclipse observations into classroom activities. Participants also valued the opportunity to contribute to authentic science through GLOBE, which involved observing atmospheric changes such as air temperature fluctuations and cloud cover alterations during the eclipse. The success of the workshop is evident in the increased confidence and skill levels of educators, as well as the publication of research posters on the GLOBE Mission Earth Student Research webpage.This session will discuss how the GLOBE Eclipse workshop series effectively utilized the unique context of the solar eclipse to enhance science education through citizen science. By adhering to the principles outlined in the Learning through Citizen Science framework, the workshop successfully engaged educators and students in meaningful scientific practices, demonstrating the potential of citizen science to enrich science education and foster a deeper understanding of Earth systems.

Jessica Taylor↗

DOE OSTI2020

Formic Acid: A Hydrogen-Bonding Cocatalyst for Formate Decomposition

Hydrogen bonding accelerates many catalytic reactions by orienting intermediates, stabilizing transition states, and even opening reaction pathways. However, most mechanistic studies regarding the decomposition of formic acid (FA), a promising hydrogen storage material, neglect hydrogen-bonding interactions even though FA is a strong hydrogen-bond donor and acceptor. Here, we probe the formation of bimolecular hydrogen-bonded complexes between FA and formate (FA–HCOO complexes) adsorbed on metal surfaces and how these complexes affect HCOO* decomposition. Using first-principles density functional theory (DFT) calculations on 12 close-packed (111)/(0001) and 8 open (100) surfaces of 12 transition metals—Ag, Au, Co, Cu, Ir, Ni, Os, Re, Pd, Pt, Rh, and Ru, we—show that FA–HCOO complexes are generally thermodynamically stable, even at elevated temperatures and pressures. We then illustrate that these complexes produce infrared spectroscopic signatures consistent with as yet unassigned experimental peaks. We last demonstrate that by stabilizing the dangling bond of monodentate HCOO*, these complexes significantly lower the barriers for rotation of HCOO* from a bidentate to a monodentate configuration, the rate-limiting step for HCOO* decomposition on many surfaces. FA thus acts as a cocatalyst for HCOO* decomposition. Our results may guide the community toward improved catalysts for reactions involving HCOO* such as FA decomposition, methanol steam reforming, and the water gas shift reaction. More broadly, our work highlights the ability of hydrogen bonding to modify the adsorbed structures of intermediates and lower the barriers for their reaction on heterogeneous catalysts. Lastly, this phenomenon can be relevant for other reactions involving ammonia, alcohols, and carboxylic acids.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2017

Optical properties of highly compressed polystyrene: An ab initio study

Using all-electron density functional theory, we have performed an ab initio study on x ray absorption spectra of highly compressed polystyrene (CH). Here, we found that the K-edge shifts in strongly coupled, degenerate polystyrene cannot be explained by existing continuum-lowering models adopted in traditional plasma physics. To gain insights into the K edge shift in warm, dense CH, we have developed a model designated as “single-mixture-in-a-box” (SMIAB), which incorporates both the lowering of continuum and the rising of Fermi surface resulting from high compression. This simple SMIAB model correctly predicts the K-edge shift of carbon in highly compressed CH in good agreement with results from quantum-molecular-dynamics (QMD) calculations. Traditional opacity models failed to give the proper K-edge shifts as the CH density increased. Based on QMD calculations, we have established a first-principles opacity table (FPOT) for CH in a wide range of densities and temperatures [p = 0.1 to 100 g/cm 3 and T = 2000 to 1,000,000 K]. The FPOT gives much higher Rosseland mean opacity compared to the cold-opacity–patched astrophysics opacity table for warm, dense CH and favorably compares to the newly improved Los Alamos ATOMIC model for moderately compressed CH (pCH ≤10 g/cm 3 ) but remains a factor of 2 to 3 higher at extremely high densities (pCH ≥ 50 g/cm 3 ). We anticipate the established FPOT of CH will find important applications to reliable designs of high-energy-density experiments. Moreover, the understanding of K-edge shifting revealed in this study could provide guides for improving the traditional opacity models to properly handle the strongly coupled and degenerate conditions.

36 MATERIALS SCIENCE↗

DOE OSTI2020

On the Convergence of Physics Informed Neural Networks for Linear Second-Order Elliptic and Parabolic Type PDEs

Physics informed neural networks (PINNs) are deep learning based techniques for solving partial differential equations (PDEs) encountered in computational science and engineering. Guided by data and physical laws, PINNs find a neural network that approximates the solution to a system of PDEs. Such a neural network is obtained by minimizing a loss function in which any prior knowledge of PDEs and data are encoded. Despite its remarkable empirical success in one, two or three dimensional problems, there is little theoretical justification for PINNs. As the number of data grows, PINNs generate a sequence of minimizers which correspond to a sequence of neural networks. We want to answer the question: Does the sequence of minimizers converge to the solution to the PDE? We consider two classes of PDEs: linear second-order elliptic and parabolic. By adapting the Schauder approach and the maximum principle, we show that the sequence of minimizers strongly converges to the PDE solution in C 0 . Furthermore, we show that if each minimizer satisfies the initial/boundary conditions, the convergence mode becomes H 1 . Computational examples are provided to illustrate our theoretical findings. To the best of our knowledge, this is the first theoretical work that shows the consistency of PINNs.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Coronary circulation: Pressure/flow parameters for assessment of ischemic heart disease

Both invasive and non-invasive parameters have been reported for assessment of the physiological status of the coronary circulation. Fractional flow reserve and coronary (or myocardial) flow reserve may be obtained by invasive or non-invasive means. These metrics of coronary stenosis severity have achieved wide clinical acceptance for guiding revascularization decisions and risk stratification. Other indices are obtained invasively (e.g., instantaneous wave-free ratio, iFR; hyperemic stenosis resistance) or non-invasively (e.g., PET absolute myocardial blood flow (mL/min/g)) and have been used for the same purposes. Both iFR, and whole-cycle distal coronary to aortic mean pressure (Pd/Pa) are measured under basal condition and used for assessment of hemodynamic stenosis severity as is index of basal stenosis resistance (BSR). These metrics typically are dichotomized at an empirically derived cut point into “normal” and “abnormal” categories for purposes of clinical decision making and data analysis. Once dichotomized the indices do not always point in the same direction and so confusion may arise. This review, therefore, will present basic principles relevant to understanding commonly employed metrics of the physiological status of the coronary circulation, potential strengths and weaknesses, and hopefully an improved appreciation of the clinical information provided by each.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2026

Photooxidation of Organic Sulfide Enhanced by Heavy Atom Effect in Porphyrin Metal–Organic Frameworks with a Sea Topology

The photoactivity of three porphyrin-based metal-organic frameworks (PMOFs) incorporating Al, Ga, and In nodes was systematically evaluated using the photooxidation of an organic sulfide (2-chloroethyl ethyl sulfide, or CEES; a mustard gas simulant). Faster photodegradation of CEES was observed for PMOFs with heavier metal nodes, placing In-PMOF as the most efficient photocatalyst in the series. Guided by this insight, we developed CSLA-10, a MOF integrating In nodes and Sn-doped porphyrin linker to synergistically amplify heavy-atom effects at both the nodes and ligand levels. CSLA-10 exhibited the fastest reported CEES photooxidation to date, achieving a half-life of 38 s in methanol under blue LED irradiation. When grafted onto textiles, CSLA-10 enabled solvent-free CEES degradation in air/O 2 with a half-life of 2.7 min and complete conversion within 7 min, representing the most rapid full degradation reported under solvent-free conditions. Furthermore, this work establishes a dual heavy-atom strategy for enhancing intersystem crossing and singlet oxygen generation in porphyrin MOFs, providing a rational design principle for next-generation photocatalysts for the degradation of toxic organic sulfides.

Metal-organic frameworks↗

NASA NTRS2004

Review of TRMM/GPM Rainfall Algorithm Validation

A review is presented concerning current progress on evaluation and validation of standard Tropical Rainfall Measuring Mission (TRMM) precipitation retrieval algorithms and the prospects for implementing an improved validation research program for the next generation Global Precipitation Measurement (GPM) Mission. All standard TRMM algorithms are physical in design, and are thus based on fundamental principles of microwave radiative transfer and its interaction with semi-detailed cloud microphysical constituents. They are evaluated for consistency and degree of equivalence with one another, as well as intercompared to radar-retrieved rainfall at TRMM's four main ground validation sites. Similarities and differences are interpreted in the context of the radiative and microphysical assumptions underpinning the algorithms. Results indicate that the current accuracies of the TRMM Version 6 algorithms are approximately 15% at zonal-averaged / monthly scales with precisions of approximately 25% for full resolution / instantaneous rain rate estimates (i.e., level 2 retrievals). Strengths and weaknesses of the TRMM validation approach are summarized. Because the dew of convergence of level 2 TRMM algorithms is being used as a guide for setting validation requirements for the GPM mission, it is important that the GPM algorithm validation program be improved to ensure concomitant improvement in the standard GPM retrieval algorithms. An overview of the GPM Mission's validation plan is provided including a description of a new type of physical validation model using an analytic 3-dimensional radiative transfer model.

Smith, Eric A.↗

NASA NTRS2019

Microgravity Liquid-Gas Two-Phase Flow: Review of Pressure Drop and Heat Transfer Correlations and Guidelines for Equipment Operability

In this report we have catalogued the flow regimes observed in microgravity, summarized correlations for the pressure drop and rate of heat transfer that are commonly used, and discuss the validation of a few correlations from available experimental results. Two-phase flow through some specific components such as bends, tees, filters and pumps are discussed from a physical perspective to guide the designer on how reduced gravity might affect their performance. Phase separation in zero gravity is addressed through the behavior and basic design concepts for devices based on passive centrifugal action, capillary forces, gas extraction through a membrane installed in a channel wall and the use of a syringe with a perforated piston to remove bubbles from small liquid volumes. We address the common instabilities that develop in flow loops owing exclusively to the two-phase nature of the flow, e.g., Ledinegg instability and concentration waves. Finally we briefly review flow metering and gauging; two-phase flow through porous media, where pressure drop and flow regime map correlations in zero-g are a current research topic; and basic operation principles of heat pipes and capillary pumped loops.

liquid-gas↗

DOE OSTI2020

Flat-histogram extrapolation as a useful tool in the age of big data

In this work, we review recent work by the authors to revisit the concept of extrapolating thermodynamic properties of classical systems using statistical mechanical principles. Specifically, we discuss how the combination of these principles with biased sampling techniques enables the prediction of free energy landscapes and other detailed information, such as structural properties, of the system in question. Remarkably accurate estimates of physical properties across a broad range of conditions have been achieved using this approach, greatly reducing the number of simulations needed to explore a given system's behaviour. While approximate, these extrapolations significantly amplify the amount of reasonably accurate information that can be extracted from simulations enabling a small set of them to feed data-intensive regression algorithms such as neural networks. Thus, this extrapolation methodology represents a useful tool for performing tasks such as high-throughput screening of physical properties, optimising force field parameters, exploring equilibrium phase behaviour, and enabling theory-guided data science for these systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2000

Structural and Machine Design Using Piezoceramic Materials: A Guide for Structural Design Engineers

Using piezoceramic materials is one way the design engineer can create structures which have an ability to both sense and respond to their environment. Piezoceramic materials can be used to create structural sensors and structural actuators. Because piezoceramic materials have transduction as a material property, their sensing or actuation functions are a result of what happens to the material. This is different than discrete devices we might attach to the structure. For example, attaching an accelerometer to a structure will yield an electrical signal proportional to the acceleration at the attachment point on the structure. Using a electromagnetic shaker as an actuator will create an applied force at the attachment point. Active material elements in a structural design are not easily modeled as providing transduction at a point, but rather they change the physics of the structure in the areas where they are used. Hence, a designer must not think of adding discrete devices to a structure to obtain an effect, but rather must design a structural system which accounts for the physical principles of all the elements in the structure. The purpose of this manual is to provide practicing engineers the information necessary to incorporate piezoelectric materials in structural design and machine design. First, we will review the solid-state physics of piezoelectric materials. Then we will discuss the physical characteristics of the electrical-active material-structural system. We will present the elements of this system which must be considered as part of the design task for a structural engineer. We will cover simple modeling techniques and review the features and capabilities of commercial design tools that are available. We will then cover practical how-to elements of working with piezoceramic materials. We will review sources of piezoceramic materials and built-up devices, and their characteristics. Finally, we will provide two design examples using piezoceramic materials, first as discrete actuators for vibration isolation, and second as structurally-distributed sensor/actuators for active acoustic control.

Inman, Daniel J.↗

DOE OSTI2024

An Adsorptive Membrane Platform for Precision Ion Separation: Membrane Design and First‐Principles Studies

One of the key challenges in separation science is the lack of precise ion separation methods and mechanistic understanding crucial for efficiently recovering critical materials from complex aqueous matrices. Herein, first-principles electronic structure calculations and in situ Raman spectroscopy are studied to elucidate the factors governing ion discrimination in an adsorptive membrane specifically designed for transition metal ion separation. Density functional theory calculations and in situ Raman data jointly reveal the thermodynamically favorable binding preferences and detailed adsorption mechanisms for competing ions. How membrane binding preferences correlate with the electronic properties of ligands is explored, such as orbital hybridization and electron localization. The findings underscore the importance of the phenolate group in oxime ligands for achieving high selectivity among competing transition metal ions. In-depth understanding on which specific atomistic site within the microenvironment of metal-ligand binding pockets governs the ion discrimination behaviors of the host will build a solid foundation to guide the rational design of next-generation materials for precision separation essential for energy technologies and environment remediation. In tandem, synthetic controllability is demonstrated to transform 3D micrometer-scale crystals to a 2D crystalline selective layer in membranes, paving the way for more precise and sustainable advances in separation science.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Atomic Edge-Guided Polyethylene Crystallization on Monolayer Two-Dimensional Materials

In this report we combine an advanced synthesis of two-dimensional (2D) materials (MoSe 2 ) having well-defined atomic edge configurations with ab initio and atomistic molecular dynamics (MD) simulations to study how atomic edges interact with polyethylene (HDPE) chains in a dilute solution assembly process. Our results reveal that Mo-terminated zigzag (Mo-ZZ) edges act as preferred nucleation sites and strongly interact with HDPE chains. The HDPE chains align in parallel with the Mo-ZZ edges and form arrays of lamellae that are perpendicular to the edges. Interestingly, atomic edge configurations are observed to dramatically change such interactions. The crystallization discrepancy at different edges was demonstrated on the same piece of MoSe 2 with different types of edges. The ab initio and MD simulations between n-alkane (n = 5 and 25), a segment of HDPE, and MoSe 2 suggest that the atomic structures of MoSe 2 can affect their interactions with n-alkane chains. Following the Mo-ZZ edge preferred nucleation principle, controlled long-range alignment of HDPE lamellae can be realized by creating multilayer MoSe 2 with parallel atomic steps. This research opens a pathway toward an atomic level understanding of polymer–2D nanomaterial interactions. It also bridges the gap between atomic-level and long-range mesoscopic structures and introduces a novel strategy for long-range structural control.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Causal discovery from data assisted by large language models

Knowledge-driven discovery of novel materials necessitates the development of causal models for property emergence. While in the classical physical paradigm, the causal relationships are deduced based on physical principles or via experiment, the rapid accumulation of observational data necessitates learning causal relationships between dissimilar aspects of material structure and functionalities based on observations. For this, it is essential to integrate experimental data with prior domain knowledge. Here, we demonstrate this approach by combining high-resolution scanning transmission electron microscopy data with insights derived from large language models (LLMs). By applying ChatGPT to domain-specific literature, such as arXiv papers on ferroelectrics, and combining the obtained information with data-driven causal discovery, we construct adjacency matrices for directed acyclic graphs that map the causal relationships between structural, chemical, and polarization degrees of freedom in Sm-doped BiFeO 3 . This approach enables us to hypothesize how synthesis conditions influence material properties and guides experimental validation. Furthermore, the ultimate objective of this work is to develop a unified framework that integrates LLM-driven literature analysis with data-driven discovery, facilitating the precise engineering of ferroelectric materials by establishing clear connections between synthesis conditions and their resulting material properties.

Causal inference↗

DOE OSTI2024

The deep-acceptor nature of the chalcogen vacancies in 2D transition-metal dichalcogenides

Abstract Chalcogen vacancies in the semiconducting monolayer transition-metal dichalcogenides (TMDs) have frequently been invoked to explain a wide range of phenomena, including both unintentional p-type and n-type conductivity, as well as sub-band gap defect levels measured via tunneling or optical spectroscopy. These conflicting interpretations of the deep versus shallow nature of the chalcogen vacancies are due in part to shortcomings in prior first-principles calculations of defects in the semiconducting two-dimensional TMDs that have been used to explain experimental observations. Here we report results of hybrid density functional calculations for the chalcogen vacancy in a series of monolayer TMDs, correctly referencing the thermodynamic charge transition levels to the fundamental band gap (as opposed to the optical band gap). We find that the chalcogen vacancies are deep acceptors and cannot lead to n-type or p-type conductivity. Both the (0/−1) and (−1/−2) transition levels occur in the gap, leading to paramagnetic charge states S = 1 / 2 and S = 1, respectively, in a collinear-spin representation. We discuss trends in terms of the band alignments between the TMDs, which can serve as a guide to future experimental studies of vacancy behavior.

36 MATERIALS SCIENCE↗

NASA NTRSDate not supplied

Hybrid Model Based Approaches for Systems Health Management and Prognostics

To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision making. In principle, data driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for an effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Hybrid Modeling↗

NASA NTRSDate not supplied

Hybrid Approaches to Systems Health Management and Prognostics

Systems Health Management↗

NASA NTRS2022

Hybrid Model Based Approaches for Systems Health Management and Prognostics

This is a previously approved and published presentation. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Present achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision-making. In principle, data-driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data-driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety-critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Prognostics↗

DOE OSTI2017

Safety Assessment of El Cabril, the Spanish Surface Disposal facility for LILW and VLLW - 17233

El Cabril project was launched in 1986, for Low and Intermediate Level Waste (LILW) disposal, based on concrete barriers and concrete disposal units. The operation of a Very Low Leve Waste (VLLW) complementary facility based on clay and High Density Polyethylene (HDPE) barriers and different types of disposal units started in 2008. Two main objectives are considered in the design: Immediate and post-closure protection and no radiological restrictions, after institutional control period. To fulfil these objectives several design criteria like waste isolation through a multi-barrier system, surveillance period no longer than 300 years and limitation of activity are applied. The Safety Assessment process follows a methodology which, taking into account important facts like safety and design criteria, time frame, radiological protection criteria, waste characteristics, environment, FEPs (features, events and processes), etc., develops different scenarios, models and calculations to define the safety approach. This safety approach follows the current guides and recommendations of international organizations, it covers the potential impact of the facility for the operational and long-term periods and it includes normal evolution and less likely accidental and inadvertent human intrusion events. All the studies and considerations done for the Safety Assessment have to be included in a safety Report, required for getting the Operation License for Nuclear Installations in Spain. The content of this Report is defined by the Spanish regulation and it has to be revised if there is a change in the facility that may affect the safety. It is also mandatory making a Safety Periodic Review (every 10 years in the case of El Cabril) that has to be presented to the Regulatory Council. This paper outlines the principles, objectives and criteria of El Cabril's disposal installation and it explains the safety assessment performed, for both LILW and VLLW disposal, as part of the documentation produced to obtain the authorization to operate. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗