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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Decoupling Failure Pathways in Li-O 2 Cells Operated Under Lean Electrolyte Conditions

The operation of lithium-oxygen (Li-O 2 ) batteries under lean electrolyte conditions offers higher energy density but leads to rapid degradation and short cycle life. Although cathode passivation and electrolyte decomposition occur in all regimes, we show that under lean electrolyte conditions, failure is primarily driven by progressive electrolyte consumption at the lithium/solid electrolyte interphase (SEI), rather than by irreversible cathode passivation. Poor wetting of the lithium surface results in heterogeneous SEI growth and high local current densities, which accelerate electrolyte loss and cell failure. Strategies aimed at improving interfacial stability, including optimized wetting and SEI forming additives, significantly extend cycle life without compromising energy density. Our results establish anode-electrolyte interactions as the dominant degradation mechanism under lean electrolyte conditions, and emphasize the need to engineer a stable Li/SEI interface for long-lasting Li-O 2 batteries.

Córdoba, Daniel [Argonne National Laboratory (ANL)↗
DOE OSTI2023

The Energy Transition: Advanced Nuclear Needed but Address Climate Vulnerabilities Now

The term “Energy Transition” is an attempt to capture an elaborate set of activities related to the modernization and decarbonization of energy grids. Performed concurrently and often in an ad hoc manner across local, state, regional and national boundaries, it is bringing chaos to what should arguably be one of the most conservatively managed of all critical infrastructure sectors. What’s more, with climate change producing an increasing tempo of extreme events, confidence in the intended resilient and redundant structure of the electric grids is likely to ebb. Even without these climate induced stressors, the nation’s electric grid was built for an earlier century. In addition to a drive towards greater efficiency via digitization and a continuing price decline in distributed energy resources (DERs), one could argue that climate change concerns are the primary driver of the energy transition. Non-CO2 emitting generation sources like wind and solar have become an important part of the overall generation fleet, albeit ones that cannot be counted upon to provide dispatchable power. Current projections indicate deployment of even larger percentages of DERs in coming years. Until far better storage capabilities arrive, the variability of wind and solar, inconsistent performance of traditional thermal generation plants, and energy delivery failures associated with natural gas pipelines will reinforce mounting reliability concerns. This pertains to both electric transmission and distribution. The recent shuttering of nuclear power plants in Germany, Japan, the US and elsewhere are also putting more downward pressure on dispatchable generation. Russia’s attack on Ukraine has roiled energy markets worldwide and forced some countries to return to coal as a primary fuel. In view circumstances such as these, it is essential that significant changes be made to policies and planning criteria, and to the standards and code on which they are based. Given the accelerating pace of extreme weather events, this needs to occur as soon as possible.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Invertible Neural Networks for Airfoil Design

We report the airfoil design problem, in which an engineer seeks a shape with desired performance characteristics, is fundamental to aerodynamics. Design workflows traditionally rely on iterative optimization methods using low-fidelity integral boundary-layer methods as higher-fidelity adjoint-based computational fluid dynamics methods are computationally expensive. Surrogate-based approaches can accelerate the design process but still rely on some iterative inverse design procedure. In this work, we leverage emerging invertible neural network (INN) tools to enable the rapid inverse design of airfoil shapes for wind turbines. INNs are specialized deep-learning models with well-defined inverse mappings. When trained appropriately, INN surrogate models are capable of forward prediction of aerodynamic and structural quantities for a given airfoil shape as well as inverse recovery of airfoil shapes with specified aerodynamic and structural characteristics. The INN approach offers a roughly 100 times speed-up compared to adjoint-based methods for inverse design. We demonstrate the INN tool for inverse design on three test cases of 100 airfoils each that satisfy the performance characteristics close to those of airfoils used in wind-turbine blades. All generated shapes satisfy the desired aerodynamic characteristics, demonstrating the success of the INN approach for inverse design of airfoils.

17 WIND ENERGY↗
DOE OSTI2024

Developing Smart Building Technology Modules to Enhance Workforce Preparedness: A Case for AI-Driven Academic and Professional Education

Smart building technologies are resources that improve building energy efficiency and resilience, reduce carbon emissions, and provide load flexibility to the grid. However, in both academic curricula and building professionals’ continuing education, there is a lack of systematic instruction on methods to integrate multiple energy systems including distributed energy resources (DER), smart building technologies, AI (Artificial Intelligence) tools and key concepts, components, and controls, including “Internet of Things” (IoT) devices. In today’s dynamic workforce, this major gap in smart building technology education prevents stakeholders from being able to attract talent with an understanding and preparation to adopt smart building technologies in building design and operations. A federally funded project included a partnership between Slipstream and Texas A&M University (TAMU) to develop a semester-long smart building curriculum for engineering college students with the ability to adapt the contents for workforce development of professionals in building services. The final product consists of 16 training videos adapted for building professionals and the public. The educational content and training materials cover the benefits of building energy systems, the latest sensor technologies and IoT devices, all with a focus on smart building technologies. The key drivers are on topics related to smart building controls (i.e., energy management information systems), smart building control platforms, cybersecurity, grid-interactive-efficient buildings (GEBs), smart building control methods, and occupant-centric control. Although not explicitly included the technologies nod to the need for AI driven technologies to prepare engineers and industry professionals to be future ready. This paper describes the project approach, provides outlines of the training materials, and identifies lessons learned in creating the content for this course. The authors suggest ways to scale the instruction of smart building concepts to empower the workforce to accelerate the adoption of smart building technologies and AI-based teaching and learning in higher education and building sector.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks and Operators in Scientific Computing: Fluid and Solid Mechanics

Abstract Advancements in computing power have recently made it possible to utilize machine learning and deep learning to push scientific computing forward in a range of disciplines, such as fluid mechanics, solid mechanics, materials science, etc. The incorporation of neural networks is particularly crucial in this hybridization process. Due to their intrinsic architecture, conventional neural networks cannot be successfully trained and scoped when data are sparse, which is the case in many scientific and engineering domains. Nonetheless, neural networks provide a solid foundation to respect physics-driven or knowledge-based constraints during training. Generally speaking, there are three distinct neural network frameworks to enforce the underlying physics: (i) physics-guided neural networks (PgNNs), (ii) physics-informed neural networks (PiNNs), and (iii) physics-encoded neural networks (PeNNs). These methods provide distinct advantages for accelerating the numerical modeling of complex multiscale multiphysics phenomena. In addition, the recent developments in neural operators (NOs) add another dimension to these new simulation paradigms, especially when the real-time prediction of complex multiphysics systems is required. All these models also come with their own unique drawbacks and limitations that call for further fundamental research. This study aims to present a review of the four neural network frameworks (i.e., PgNNs, PiNNs, PeNNs, and NOs) used in scientific computing research. The state-of-the-art architectures and their applications are reviewed, limitations are discussed, and future research opportunities are presented in terms of improving algorithms, considering causalities, expanding applications, and coupling scientific and deep learning solvers.

Computer Science↗
DOE OSTI2016

TU-AB-BRC-10: Modeling of Radiotherapy Linac Source Terms Using ARCHER Monte Carlo Code: Performance Comparison of GPU and MIC Computing Accelerators

Purpose: (1) To perform phase space (PS) based source modeling for Tomotherapy and Varian TrueBeam 6 MV Linacs, (2) to examine the accuracy and performance of the ARCHER Monte Carlo code on a heterogeneous computing platform with Many Integrated Core coprocessors (MIC, aka Xeon Phi) and GPUs, and (3) to explore the software micro-optimization methods. Methods: The patient-specific source of Tomotherapy and Varian TrueBeam Linacs was modeled using the PS approach. For the helical Tomotherapy case, the PS data were calculated in our previous study (Su et al. 2014 41(7) Medical Physics). For the single-view Varian TrueBeam case, we analytically derived them from the raw patient-independent PS data in IAEA’s database, partial geometry information of the jaw and MLC as well as the fluence map. The phantom was generated from DICOM images. The Monte Carlo simulation was performed by ARCHER-MIC and GPU codes, which were benchmarked against a modified parallel DPM code. Software micro-optimization was systematically conducted, and was focused on SIMD vectorization of tight for-loops and data prefetch, with the ultimate goal of increasing 512-bit register utilization and reducing memory access latency. Results: Dose calculation was performed for two clinical cases, a Tomotherapy-based prostate cancer treatment and a TrueBeam-based left breast treatment. ARCHER was verified against the DPM code. The statistical uncertainty of the dose to the PTV was less than 1%. Using double-precision, the total wall time of the multithreaded CPU code on a X5650 CPU was 339 seconds for the Tomotherapy case and 131 seconds for the TrueBeam, while on 3 5110P MICs it was reduced to 79 and 59 seconds, respectively. The single-precision GPU code on a K40 GPU took 45 seconds for the Tomotherapy dose calculation. Conclusion: We have extended ARCHER, the MIC and GPU-based Monte Carlo dose engine to Tomotherapy and Truebeam dose calculations.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Nano-Engineered Interfaces in Dual-Layer Electrodes for Protonic Ceramic Cells with Enhanced Stability and Kinetics

Enhancing interfacial stability and charge transfer in protonic ceramic cells (PCCs) remains a critical challenge, as structural degradation and interfacial resistance often compromise durability and efficiency. Here, we report a nanoengineered dual-layer oxygen electrode architecture designed to address these limitations by introducing a fine-grained nanoparticle interfacial contact layer beneath a porous catalytic backbone. The nanoscale powders, through enhanced sintering activity, densify into a robust interfacial layer that promotes strong chemical bonding, uniform adhesion, and continuous ionic/electronic pathways with the BCZYYb electrolyte. This hierarchical architecture mitigates delamination, redistributes mechanical stress, and establishes efficient charge and mass transport channels without relying on corrosive surface treatments. Electrochemical evaluation demonstrates that the dual-layer design markedly reduces interfacial polarization resistance and accelerates electrode kinetics. Compared to the single-layer counterpart, the architecture achieves a peel strength of 44.53 N/cm 2 , a 40% improvement in peak power density (0.96 W cm –2 at 600 °C), and a 130% enhancement in electrolysis current density (4.78 A cm –2 at 1.57 V). Faradaic efficiency remains as high as 88% under high steam concentrations, underscoring minimal charge loss during practical operation. Notably, the electrode retains stability across 450–600 °C and under transient voltage cycling, with impedance spectra confirming suppressed interfacial resistance growth over prolonged use. These results highlight nanoscale interface engineering as a powerful route to enhance both mechanical robustness and electrochemical kinetics in PCCs. The demonstrated scalability and durability of this architecture provide a versatile platform for advancing solid-state electrochemical systems, including reversible fuel cells and high-efficiency hydrogen production technologies.

Faradaic efficiency↗
DOE OSTI2017

Systematic Parameterization of Lignin for the CHARMM Force Field

Plant cell walls have three primary components, cellulose, hemicellulose, and lignin, the latter of which is a recalcitrant, aromatic heteropolymer that provides structure to plants, water and nutrient transport through plant tissues, and a highly effective defense against pathogens. Overcoming the recalcitrance of lignin is key to effective biomass deconstruction, which would in turn enable the use of biomass as a feedstock for industrial processes. Our understanding of lignin structure in the plant cell wall is hampered by the limitations of the available lignin forcefields, which currently only account for a single linkage between lignins and lack explicit parameterization for emerging lignin structures both from natural variants and engineered lignin structures. Since polymerization of lignin occurs via radical intermediates, multiple C-O and C-C linkages have been isolated , and the current force field only represents a small subset of lignin the diverse lignin structures found in plants. In order to take into account the wide range of lignin polymerization chemistries, monomers and dimer combinations of C-, H-, G-, and S-lignins as well as with hydroxycinnamic acid linkages were subjected to extensive quantum mechanical calculations to establish target data from which to build a complete molecular mechanics force field tuned specifically for diverse lignins. This was carried out in a GPU-accelerated global optimization process, whereby all molecules were parameterized simultaneously using the same internal parameter set. By parameterizing lignin specifically, we are able to more accurately represent the interactions and conformations of lignin monomers and dimers relative to a general force field. This new force field will enables computational researchers to study the effects of different linkages on the structure of lignin, as well as construct more accurate plant cell wall models based on observed statistical distributions of lignin that differ between disparate feedstocks, and guide further lignin engineering efforts.

09 BIOMASS FUELS↗
DOE OSTI2023

Eco–Friendly Solvent Engineered CsPbI 2.77 Br 0.23 Ink for Large–Area and Scalable High Performance Perovskite Solar Cells

The performance of large-area perovskite solar cells (PSCs) has been assessed for typical compositions, such as methylammonium lead iodide (MAPbI 3 ), using a blade coater, slot-die coater, solution shearing, ink-jet printing, and thermal evaporation. However, the fabrication of large-area all-inorganic perovskite films is not well developed. This study develops, for the first time, an eco-friendly solvent engineered all-inorganic perovskite ink of dimethyl sulfoxide (DMSO) as a main solvent with the addition of acetonitrile (ACN), 2-methoxyethanol (2-ME), or a mixture of ACN and 2-ME to fabricate large-area CsPbI 2.77 Br 0.23 films with slot-die coater at low temperatures (40–50 °C). The perovskite phase, morphology, defect density, and optoelectrical properties of prepared with different solvent ratios are thoroughly examined and they are correlated with their respective colloidal size distribution and solar cell performance. Here, the optimized slot-die-coated CsPbI 2.77 Br 0.23 perovskite film, which is prepared from the eco-friendly binary solvents dimethyl sulfoxide:acetonitrile (0.8:0.2 v/v), demonstrates an impressive power conversion efficiency (PCE) of 19.05%. Moreover, the device maintains ≈91% of its original PCE after 1 month at 20% relative humidity in the dark. It is believed that this study will accelerate the reliable manufacturing of perovskite devices.

binary and ternary solvent↗
DOE OSTI2015

Electrochemical Sensors and Biosensors Based on Nanomaterials and Nanostructures

Taking advantages of exceptional attributes, such as easy-to-operate, economical, sensitive, portable, and simple-to- construct, considerable attention has been devoted to the integration of recognition elements with electronic elements to develop electrochemical sensors and biosensors. Recent decades tremendous amounts of attention have been witnessed in this active area. Various electrochemical devices such as amperometric sensors, electrochemical impedance sensors, electrochemical luminescence sensors as well as photoelectrochemical sensors, provide wide applications of detecting chemical and biological targets in terms of electrochemical change of electrode interfaces. With remarkable achievements in nanotechnology and nanoscience, nanomaterial-based electrochemical signal amplifications have great potential of improving both in sensitivity and selectivity for electrochemical sensors and biosensors. First of all, it is well known that the electrode materials play a critical role in the construction of high-performance electrochemical sensing platforms for detecting target molecules through various analytical principles. Furthermore, in addition to electrode materials, functional nanomaterials can not only produce a synergic effect among catalytic activity, conductivity and biocompatibility to accelerate the signal transduction, but also amplify biorecognition events by specifically designed signal tags, leading to highly sensitive biosensing. Sig-nificantly, extensive research on construction of functional electrode materials, coupled with numerous electrochemical methods, is advancing wide applications of electrochemical devices. For example, Walcarius et al. highlighted the recent advances of nano-objects, nano-engineered and/or nanostructured materials for the rational design of bio-functionalized electrodes and related (bio)sensing systems.1 The attractiveness of such nanomaterials relies on their ability to act as effective immobilization matrices, and their intrinsic and unique features as described above. These features combined with the functioning of biomolecules contribute to the improvement of bioelectrode performance in terms of sensitivity and specificity. Our group recently presented a general overview of nanomaterial-enhanced paper-based biosensors including lateral-flow test-strip, and paper microfluidic devices.2With different kinds of nanoparticles (NPs), paper-based biosensor devices have shown great potential in the enhancement of sensitivity and specificity of disease diagnosis in developing countries. This review focuses on recent advances in electrochemical sensors and biosensors based on nanomaterials and nanostructures during 2013 to 2014. The aim of this effort is to provide the reader with a clear and concise view of new advances in areas ranging from electrode engineering, strategies for electrochemical signal amplification, and novel electroanalytical techniques used to the miniaturization and integration of the sensors. Moreover, the authors have attempted to highlight areas of latest and significant development of enhanced elec-trochemical nanosensors and nanobiosensors that inspire broader interests across various disciplines. Electrochemical sensors for small molecules, enzyme-based biosensors, genosensors, immunosensors and cytosensors are reviewed herein (Figure 1). Such novel advances are important for the development of electrochemical sensors as that open up new avenues and methods for future research. Readers interested in the general principles of electrochemical sensors and electrochemical methods are recommended to refer to other excellent literature for a broad scope on this area.3-4 However, due to the explosion of publications in this active field, we do not claim that this review includes all of the published works in the past two years. Due to the large activity in this field we apologize to the authors of excellent work, which is unintentionally left out.

Zhu, Chengzhou↗
DOE OSTI2019

Toward the implementation of self-powered, wireless, real-time reactor power sensing

The successful development of real-time, self-powered, wireless reactor power sensing technology would enable key operating parameters of every fuel assembly in a commercial reactor core to be continuously monitored, and potentially be utilized for applications such as closed-loop reactor control. This paper discusses the expansion of successful work on the application of Self Powered Neutron Detectors (SPND) for reactor power monitoring and the wireless transmission of these detector signals using advanced vacuum micro-electronic (VME) transmitter technology developed by Westinghouse in collaboration with the Penn State University Radiation Science and Engineering Center. Results demonstrate that this technology may be successfully applied via long-term testing in the Penn State TRIGA reactor. SPND are inherently slower than, for example, ion chambers due to their dependence on radioactive decay to generate a signal. Additional research developed delay compensation algorithms to accelerate the response of these detectors for use in near-real-time closed-loop reactor control. Initial results indicate that practical signal acceleration can be obtained digitally or with an equivalent analog circuit. Future research will apply an inverse model of the sensor’s balance differential equations to more-effectively accelerate the SPND signal. Use of inverse models will also prove to be adaptive in the sense that time-dependent burn-up of these detectors, due to neutron activation of the detector material, can be modeled and applied in the acceleration algorithm.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Electro-activated indigos intensify ampere-level CO 2 reduction to CO on silver catalysts

The electrochemical reduction of carbon dioxide (CO 2 ) to carbon monoxide (CO) is challenged by a selectivity decline at high current densities. Here we report a class of indigo-based molecular promoters with redox-active CO 2 binding sites to enhance the high-rate conversion of CO 2 to CO on silver (Ag) catalysts. Theoretical calculations and in situ spectroscopy analyses demonstrate that the synergistic effect at the interface of indigo-derived compounds and Ag nanoparticles could activate CO 2 molecules and accelerate the formation of key intermediates (*CO 2 – and *COOH) in the CO pathway. Indigo derivatives with electron-withdrawing groups further reduce the overpotential for CO production upon optimizing the interfacial CO 2 binding affinity. By integrating the molecular design of redox-active centres with the defect engineering of Ag structures, we achieve a Faradaic efficiency for CO exceeding 90% across a current density range of 0.10 − 1.20 A cm –2 . The Ag mass activity toward CO increases to 174 A mg –1 Ag . This work showcases that employing redox-active CO 2 sorbents as surface modification agents is a highly effective strategy to intensify the reactivity of electrochemical CO 2 reduction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Towards large phase space beams at the CEBAF injector

We report on the status of a degrader device to generate large phase space beams for machine acceptance studies in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. The degrader device consists of thin, low-Z targets to degrade the electron beam phase space through multiple scattering, two apertures to define the maximum transverse emittance, and a solenoid to aid in matching to the rest of the injector beamline. The engineering design of the degrader device and projected degraded beam phase space parameters are presented.

Sy, A.↗
DOE OSTI2017

Up-skilling and Re-skilling for the Nuclear Industry - 17276

In the UK, nuclear skills and qualifications have been the subject of several studies in higher levels of Government, including one by UK Cabinet Office. The newly formed UK Nuclear Skills Strategy Group (NSSG) has been tasked with addressing the current skills and people gaps for the UK nuclear industry. The NSSG is working with several key organisations in the UK: The National Skills Academy for Nuclear, (NSAN), which covers skills interventions, training and workforce development and experience, and the newly forming National College for Nuclear (NCfN) which will become a nuclear qualifications awarding body, regulating educational qualifications and curriculum in the nuclear sector. The NSSG has published its own UK Nuclear Skills Strategy in December 2016, aligned to the UK Cabinet Office study. It is the intent that, in the near future, nuclear qualifications and experience will be directly transferable across different organisations and nuclear sites in the UK, enabling the maintenance of high standards of behaviours and improved mobility throughout the sector. The UK needs to maintain and grow a diverse workforce and nuclear Suitably Qualified and Experienced Personnel (SQEP) people. The 2015 UK Nuclear Labour Market Intelligence (LMI) report, produced by Cogent for the Nuclear Energy Skills Alliance (NESA), concluded that the total nuclear workforce demand for the UK Nuclear Civil and Defence sector needed to grow by 33,000 people in the next five years, which includes replacement and expansion demand. This involved all disciplines and grades, with for example, higher level skilled workers needing to increase by 2200 FTEs every year. The main discipline pinch points were in, for example, Mechanical; Electrical; Project Management; Safety Case Specialists; Commissioning Engineers. Of the overall workforce, around 10% is over 60 years old and around 38% is over 50 years old. Jacobs in the UK is widely involved in many aspects of skills and competence development, with active involvement in local and national training programmes, and driving new methodologies to accelerate our staff's journey to nuclear competence. As well as looking at the immediate future, there is a risk in the UK that, over the 120-year plus clean-up mission, there won't be enough people with the right skills. Jacobs is developing sustainable solutions to this in our skills and development strategy for the nuclear sector. Jacobs has developed a variety of approaches to help us maintain and grow our diverse workforce and nuclear 'SQEP' people. This includes: - Identifying and developing our employees' technical and personal skills and capabilities, particularly the requirements of our millennials in both technical and social aspects of their working lives. - Identifying and planning for our future skills needs to meet our projected business requirements. - Our programme to transfer skilled employees from other sectors into the nuclear industry. - Our extensive school student placement scheme, aligned to the UK's 'Industrial Cadet' scheme, which has helped to bring school-aged students into Jacobs on workplace experience. - Our collaboration with our customers to develop UK national level Trailblazer Apprentice programmes. - Our involvement at a UK national level in the work of the National Skills Academy for Nuclear (NSAN), the Nuclear Energy Skills Alliance (NESA) High Level Skills Committee and the newly forming UK National College for Nuclear (NCfN). - Our work to ensure diversity is valued in our workplace, for example in our involvement in 'Women in Nuclear'. (authors)

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2015

Stand-Alone Dynamic System Simulation of a Fissile Solution System

Dynamic system simulations (DSS) of fissile solution systems (FSS) have been performed at Los Alamos National Laboratory (LANL) for several years. DSS have been designed and executed using the DESIRE (Direct Executing Simulation in Real Time) system, a platform ideal for expert system modelers, as it allows for direct mathematical specification of a model, and facilities for performing experiments and analyses with the model. Models built in DESIRE have been compared to historical data from several historic aqueous homogeneous reactors (AHR) showing close agreement between experimental data and model estimates. Historical systems modeled include: SUPO (Super Power), an AHR that operated at Los Alamos National Laboratory from 1951 to 1974; SILENE, which was operated by CEA at Valduc, France until 2011; KEWB (Kinetics Experiment Water Boiler) A-2 and B-5 cores, operated by North American Atomics during the 1960’s and 1970’s. SUPO is considered a benchmark for steady-state operations, SILENE for pulse, and KEWB as confirmatory for both modes. However, there are some simulation tasks that are not well-suited to DESIRE model implementation. These tasks include engineering design and analysis and providing a hardware simulator backend. It has been determined that executable simulation models would better suit these purposes, hence a process for converting DESIRE simulation models to executable simulation models was designed and implemented, and several models have been converted. DESIRE simulation models are converted to C++ executable models by an automated process. The conversion process is quick, accurate and repeatable, giving confidence that converted models will perform as well as the original DESIRE models. Extensive testing on the SUPO and accelerator driven subcritical system models has shown that the executable models perform almost identically to the DESIRE models. All major trends and events are simulated with excellent agreement and the steady-state results typically agree within a small fraction of a percent (always better than 0.1% and generally better than 0.01% agreement).

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗