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All sourcesNASA NTRSDOE OSTI
DOE OSTI2011

Country Review of Energy-Efficiency Financial Incentives in the Residential Sector

A large variety of energy-efficiency policy measures exist. Some are mandatory, some are informative, and some use financial incentives to promote diffusion of efficient equipment. From country to country, financial incentives vary considerably in scope and form, the type of framework used to implement them, and the actors that administer them. They range from rebate programs administered by utilities under an Energy-Efficiency Resource Standards (EERS) regulatory framework (California, USA) to the distribution of Eco-points rewarding customers for buying highly efficient appliances (Japan). All have the primary objective of transforming the current market to accelerate the diffusion of efficient technologies by addressing up-front cost barriers faced by consumers; in most instances, efficient technologies require a greater initial investment than conventional technologies. In this paper, we review the different market transformation measures involving the use of financial incentives in the countries belonging to the Major Economies Forum. We characterize the main types of measures, discuss their mechanisms, and provide information on program impacts to the extent that ex-ante or ex-post evaluations have been conducted. Finally, we identify best practices in financial incentive programs and opportunities for coordination between Major Economies Forum countries as envisioned under the Super Efficient Appliance Deployment (SEAD) initiative.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Slag Management of Carbon Feedstock Used in Gasification FY17 TCF between Eastman Chemical and NETL (Final Report)

NETL, in collaboration with Eastman Chemical Corp (Kingsport, TN), targeted the joint evaluation and refinement of a slag management program developed by NETL (NETL IP, patent applied for) to control gasifier slag properties formed from ash in individual or combinations of different coal-based carbon feedstock materials and specific slag modifiers used in gasification. Slag forms from mineral or organic-metallic impurities in the carbon feedstock that melt at the high temperatures of gasification, coalescing to form a liquid (slag) that must flow from the gasifier. Slag additives, also known as slag modifiers, are often added to the carbon feedstock to modify the ash chemistry and achieve desired slag flow properties at target temperatures used in gasification. If slag does not flow from a gasifier, its buildup within the gasification chamber will cause either gasifier shutdown to physically remove the slag (consuming time and damaging the protective refractory lining), or the gasification temperature must be increased to lower slag viscosity so it can flow from the gasifier, leading to greater refractory wear/corrosion, along with increased fouling and downstream component wear. Because of these issues, gasification is typically targeted at the lowest temperature possible where slag will flow in a controlled manner (a temperature range called T100 for the lowest temperature, with T250 being the highest temperature where the slag has achieved the desired fluidity). That temperature range also needs to yield a desired syngas composition, highlighting the importance of knowing and being able to control carbon feedstock slag viscosity and the temperature of thermal breakdown of the carbon feedstock. Slag viscosity varies with changes in the gasification chamber temperature, oxygen partial pressure, and slag chemistry. The slag model work with Eastman Chemical targeted predicting and controlling molten coal ash slag behavior at specific gasification conditions. The final goal of the refined model was to develop/refine the NETL slag model so a gasifier operator would be able to predict slag behavior prior to purchasing or using a carbon feedstock and control slag properties once a carbon feedstock is being gasified, allowing a gasifier user to determine what mineral additives or blends of different carbon materials would be needed to achieve desired slag properties at targeted gasification conditions. For a given carbon feedstock and desired gasification condition, the actual gasification cost and slag behavior would be able to be predicted using the slag management program in advance of purchasing or using the carbon feedstock – allowing accurate control of slag ash behavior in real time. As part of the TCF project work with Eastman Chemical, the viscosity of several ash compositions of interest to them would be measured and used to populate the slag model, along with a large database of historical slag temperature, chemistry, and viscosity information they possess – information that would in-crease the predictive accuracy of the slag management model with slag ash chemistry of interest to them. As part of the model evaluation and refinement, the predictive model and its database will be installed on computers at Eastman Chemical and used to manage slag properties in a gasifier during real-time operation of their gasifier. In summary, success of the slag management program at Eastman Chemical would support its use by other gasifier operators to manage slag chemistry and properties over other desired gasification temperature ranges, allowing greater process control and efficiency of their carbon feedstock conversion during gasification.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2017

Chapter 21: Estimating Net Savings - Common Practices. The Uniform Methods Project: Methods for Determining Energy Efficiency Savings for Specific Measures

This chapter focuses on the methods used to estimate net energy savings in evaluation, measurement, and verification (EM and V) studies for energy efficiency (EE) programs. The chapter provides a definition of net savings, which remains an unsettled topic both within the EE evaluation community and across the broader public policy evaluation community, particularly in the context of attribution of savings to a program. The chapter differs from the measure-specific Uniform Methods Project (UMP) chapters in both its approach and work product. Unlike other UMP resources that provide recommended protocols for determining gross energy savings, this chapter describes and compares the current industry practices for determining net energy savings but does not prescribe methods.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2011

Developing State and National Evaluation Infrastructures- Guidance for the Challenges and Opportunities of EM&V

Evaluating the impacts and effectiveness of energy efficiency programs is likely to become increasingly important for state policymakers and program administrators given legislative mandates and regulatory goals and increasing reliance on energy efficiency as a resource. In this paper, we summarize three activities that the authors have conducted that highlight the expanded role of evaluation, measurement and verification (EM&V): a study that identified and analyzed challenges in improving and scaling up EM&V activities; a scoping study that identified issues involved in developing a national efficiency EM&V standard; and lessons learned from providing technical assistance on EM&V issues to states that are ramping up energy efficiency programs. The lessons learned are summarized in 13 EM&V issues that policy makers should address in each jurisdiction and which are listed and briefly described. The paper also discusses how improving the effectiveness and reliability of EM&V will require additional capacity building, better access to existing EM&V resources, new methods to address emerging issues and technologies, and perhaps foundational documents and approaches to improving the credibility and cross jurisdictional comparability of efficiency investments. Two of the potential foundational documents discussed are a national EM&V standard or resource guide and regional deemed savings and algorithm databases.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2015

Advanced Hydrogen Turbine Development

Under the sponsorship of the U.S. Department of Energy (DOE) National Energy Technology Laboratories, Siemens has completed the Advanced Hydrogen Turbine Development Program to develop an advanced gas turbine for incorporation into future coal-based Integrated Gasification Combined Cycle (IGCC) plants. All the scheduled DOE Milestones were completed and significant technical progress was made in the development of new technologies and concepts. Advanced computer simulations and modeling, as well as subscale, full scale laboratory, rig and engine testing were utilized to evaluate and select concepts for further development. Program Requirements of: A 3 to 5 percentage point improvement in overall plant combined cycle efficiency when compared to the reference baseline plant; 20 to 30 percent reduction in overall plant capital cost when compared to the reference baseline plant; and NOx emissions of 2 PPM out of the stack. were all met. The program was completed on schedule and within the allotted budget

42 ENGINEERING↗
DOE OSTI2021

Industrial Energy Assessment Training Effectiveness Evaluation: An Eye-Tracking Study

It is essential to understand the effectiveness of any training program so it can be improved accordingly. Various studies have applied standard metrics for the evaluation of visual behavior to recognize the areas of interest that attract individuals’ attention as there is a high correlation between attentional behavior and where one is focusing on. However, through reviewing the literature, we believe that studies that applied eye-tracking technologies for training purposes are still limited, especially in the industrial energy assessment training field. In this paper, the effectiveness of industrial energy assessment training was quantitatively evaluated by measuring the attentional allocation of trainees using eye-tracking technology. Moreover, this study identifies the areas that require more focus based on evaluating the performance of subjects after receiving the training. Additionally, this research was conducted in a controlled environment to remove the distractions that may be caused by environmental factors to only concentrate on variables that influence the learning behavior of subjects. The experiment results showed that after receiving the training, the subjects’ performance in energy assessment was significantly improved in two areas: production, and recycling and waste management, and the designed training program enhanced the knowledge of participants in identifying energy-saving opportunities to the knowledge level of experienced participants.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Analysis of Space-Conditioning Loads in Commercial Buildings

Space conditioning end-uses, which include heating, cooling, and ventilation, represent a significant fraction of commercial building energy use, with a wide variety of heating and cooling technology options available in the market. In the interest of improving the overall efficiency of heating, ventilation and air-conditioning (HVAC) technologies, governments, utilities and private sector entities have implemented a variety of market transformation policies that aim to influence consumer purchase decisions. To evaluate the costs and benefits of such programs, analysts typically postulate a hypothetical default equipment choice, and compare it to one that provides comparable service with lower energy and/or power use. The corresponding reduced operating cost provides a benefit that offsets the potential higher cost of improved efficiency. Typically, life-cycle cost or cash-flow analyses are used to quantify the net economic benefit. These analyses require the capability to assess how a given equipment design would perform across a broad range of characteristics, both of the building and of the local weather. While these assessments can be performed using customized building simulations, it is generally not practical to develop and validate detailed building simulation code to cover all the potential variations of equipment design and installation. An alternative, and somewhat simpler, approach is to solely use detailed building simulations to generate time series of heating and cooling loads in commercial buildings. These loads can then be used as input to more detailed, stand-alone engineering models that simulate HVAC system performance under different equipment designs. This approach was used to evaluate a range of high-efficiency commercial packaged air conditioner design options for the Department of Energy’s Appliance and Equipment Standards Program (DOE-EERE 2015). While there may be some loss of precision relative to full simulation, the accuracy of this approach is sufficient for practical applications of cost-benefit analysis. This report describes the development of a database of commercial building heating and cooling loads, generated using the EnergyPlus software package, a whole building energy use model supported by the Department of Energy (DOE-EERE 2020a). EnergyPlus takes as input a set of configuration files that describe the building itself (size, zoning, envelope characteristics, etc.) and the various systems within it (HVAC, lighting, water heating, etc.). This analysis uses a publicly available collection of commercial reference buildings (CRB), comprised of sixteen building types and three vintages (DOE-EERE 2020b; Deru et al 2011). Each building is simulated in eighteen different locations, covering a wide range of climatic conditions. The prototype building description files assign the type of HVAC equipment used, and capacities across climate zones.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

Parallel-scanning tomosynthesis using a slot scanning technique: Fixed-focus reconstruction and the resulting image quality

Purpose: Parallel-scanning tomosynthesis (PS-TS) is a novel technique that fuses the slot scanning technique and the conventional tomosynthesis (TS) technique. This approach allows one to obtain long-view tomosynthesis images in addition to normally sized tomosynthesis images, even when using a system that has no linear tomographic scanning function. The reconstruction technique and an evaluation of the resulting image quality for PS-TS are described in this paper. Methods: The PS-TS image-reconstruction technique consists of several steps (1) the projection images are divided into strips, (2) the strips are stitched together to construct images corresponding to the reconstruction plane, (3) the stitched images are filtered, and (4) the filtered stitched images are back-projected. In the case of PS-TS using the fixed-focus reconstruction method (PS-TS-F), one set of stitched images is used for the reconstruction planes at all heights, thus avoiding the necessity of repeating steps (1)–(3). A physical evaluation of the image quality of PS-TS-F compared with that of the conventional linear TS was performed using a R/F table (Sonialvision safire, Shimadzu Corp., Kyoto, Japan). The tomographic plane with the best theoretical spatial resolution (the in-focus plane, IFP) was set at a height of 100 mm from the table top by adjusting the reconstruction program. First, the spatial frequency response was evaluated at heights of −100, −50, 0, 50, 100, and 150 mm from the IFP using the edge of a 0.3-mm-thick copper plate. Second, the spatial resolution at each height was visually evaluated using an x-ray test pattern (Model No. 38, PTW Freiburg, Germany). Third, the slice sensitivity at each height was evaluated via the wire method using a 0.1-mm-diameter tungsten wire. Phantom studies using a knee phantom and a whole-body phantom were also performed. Results: The spatial frequency response of PS-TS-F yielded the best results at the IFP and degraded slightly as the distance from the IFP increased. A visual evaluation of the spatial resolution using the x-ray test pattern indicated that the resolution was 1.8 lp/mm at the IFP and 1.2 lp/mm at heights of −100 and 100 mm from the IFP. The authors demonstrated that a spatial resolution of 1.2–1.8 lp/mm could be obtained within heights of 200 mm of the IFP. The slice sensitivity varied between 11.1 and 13.8 mm for heights between −50 and 100 mm, and there was no critical change in the slice sensitivity within a height range of 150 mm around the IFP. The phantom results demonstrated that tomosynthesis and long-view images could be reconstructed. Conclusions: PS-TS-F provides tomosynthesis images while using low-cost systems that have no tomographic scanning function, such as tableside-controlled universal R/F systems or universal radiographic systems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

RAVEN User Manual

RAVEN is a generic software framework to perform parametric and probabilistic analysis based on the response of complex system codes. The initial development was aimed to provide dynamic risk analysis capabilities to the Thermo-Hydraulic code RELAP-7, currently under development at the Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose probabilistic and uncertainty quantification platform, capable to agnostically communicate with any system code. This agnosticism includes providing Application Programming Interfaces (APIs). These APIs are used to allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by inputs files or via python interfaces. RAVEN is capable of investigating the system response, and investigating the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused toward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. The development of RAVEN has started in 2012, when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework became stronger. RAVEN principal assignment is to provide the necessary software and algorithms in order to employ the concept developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just the individuation of the frequency of an event potentially leading to a system failure, but the closeness (or not) to key safety-related events. Hence, the approach is interested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. for an important process such as peak pressure in a pipe) is exceeded under certain conditions. The initial development of RAVEN has been focused on providing dynamic risk assessment capability to RELAP-7, currently under development at the INL and, likely, future replacement of the RELAP5-3D code. Most the capabilities that have been implemented having RELAP-7 as principal focus are easily deployable for other system codes. For this reason, several side activaties are currently ongoing for coupling RAVEN with software such as RELAP5-3D, etc. The aim of this document is the explanation of the input requirements, focalizing on the input structure.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Power System Wildfire Risks and Potential Solutions: A Literature Review & Proposed Metric

Several fire risk evaluation, fire tracking, and fire response resources are available. The risk metrics and fire response programs are sometimes modified to include a power system context. The risk metrics often evaluate the risk of fires causing power system faults or outages, especially on transmission systems. The response programs are modified to ensure the safety of power system equipment and first responders as well as to coordinate power system outages to both ensure safety during an active fire and prevent fire ignition during high risk periods. Although some aspects of wildfire responses have been adapted to include power system concerns, adaptations to power system operations and maintenance to include wildfire risks and responses are still nascent. In particular, a risk metric that evaluates the potential for power system components to ignite wildfires is needed to help guide power system upgrade efforts and power system fire safety measures. This document serves as a brief literature review of wildfire risk metrics and response programs and how they relate to power systems. It also includes a proposed risk metric and structure for describing the risk of a power system component igniting a fire.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Innovation Incubator: Whisker Labs Technical Evaluation

The Wells Fargo Innovation Incubator (IN2) is a program to foster and accelerate startup companies with commercial building energy-efficiency and demand management technologies. The program is funded by the Wells Fargo Foundation and co-administered by the National Renewable Energy Laboratory (NREL). Whisker Labs, an Oakland, California-based company, was one of four awardees in the first IN2 cohort and was invited to participate in the program because of its novel electrical power sensing technology for circuit breakers. The stick-on Whisker meters install directly on the front face of the circuit breakers in an electrical panel using adhesive, eliminating the need to open the panel and install current transducers (CTs) on the circuit wiring.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Using remote sensing to quantify the additional climate benefits of California forest carbon offset projects

Abstract Nature‐based climate solutions are a vital component of many climate mitigation strategies, including California's, which aims to achieve carbon neutrality by 2045. Most carbon offsets in California's cap‐and‐trade program come from improved forest management (IFM) projects. Since 2012, various landowners have set up IFM projects following the California Air Resources Board's IFM protocol. As many of these projects approach their 10th year, we now have the opportunity to assess their effectiveness, identify best practices, and suggest improvements toward future protocol revisions. In this study, we used remote sensing‐based datasets to evaluate the carbon trends and harvest histories of 37 IFM projects in California. Despite some current limitations and biases, these datasets can be used to quantify carbon accumulation and harvest rates in offset project lands relative to nearby similar “control” lands before and after the projects began. Five lines of evidence suggest that the carbon accumulated in offset projects to date has generally not been additional to what might have otherwise occurred: (1) most forests in northwestern California have been accumulating carbon since at least the mid‐1980s and continue to accumulate carbon, whether enrolled in offset projects or not; (2) harvest rates were high in large timber company project lands before IFM initiation, suggesting they are earning carbon credits for forests in recovery; (3) projects are often located on lands with higher densities of low‐timber‐value species; (4) carbon accumulation rates have not yet increased on lands that enroll as offset projects, relative to their pre‐enrollment levels; and (5) harvest rates have not decreased on most project lands since offset project initiation. These patterns suggest that the current protocol should be improved to robustly measure and reward additionality. In general, our framework of geospatial analyses offers an important and independent means to evaluate the effectiveness of the carbon offsets program, especially as these data products continue improving and as offsets receive attention as a climate mitigation strategy.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

RAVEN User Manual

RAVEN is a generic software framework to perform parametric and probabilistic analy- sis based on the response of complex system codes. The initial development was aimed to provide dynamic risk analysis capabilities to the Thermo-Hydraulic code RELAP-7, currently under development at the Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose probabilistic and uncer- tainty quantification platform, capable to agnostically communicate with any system code. This agnosticism includes providing Application Programming Interfaces (APIs). These APIs are used to allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by inputs files or via python interfaces. RAVEN is capable of investigating the system response, and investigating the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused to- ward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. The development of RAVEN has started in 2012, when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework became stronger. RAVEN principal assignment is to provide the necessary software and algorithms in order to employ the concept developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just the individuation of the frequency of an event potentially leading to a system failure, but the closeness (or not) to key safety-related events. Hence, the approach is in- terested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. for an important process such as peak pressure in a pipe) is exceeded under certain conditions. The initial development of RAVEN has been focused on providing dynamic risk assess- ment capability to RELAP-7, currently under develop-ment at the INL and, likely, future replacement of the RELAP5-3D code. Most the capabilities that have been implemented having RELAP-7 as principal focus are easily deployable for other system codes. For this reason, several side activates are currently ongoing for coupling RAVEN with soft- ware such as RELAP5-3D, etc. The aim of this document is the explaination of the input requirements, focalizing on the input structure.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

RAVEN User Manual

RAVEN is a generic software framework to perform parametric and probabilistic analy- sis based on the response of complex system codes. The initial development was aimed to provide dynamic risk analysis capabilities to the Thermo-Hydraulic code RELAP-7, currently under development at the Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose probabilistic and uncer- tainty quantification platform, capable to agnostically communicate with any system code. This agnosticism includes providing Application Programming Interfaces (APIs). These APIs are used to allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by inputs files or via python interfaces. RAVEN is capable of investigating the system response, and investigating the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused to- ward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. The development of RAVEN has started in 2012, when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework became stronger. RAVEN principal assignment is to provide the necessary software and algorithms in order to employ the concept developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just the individuation of the frequency of an event potentially leading to a system failure, but the closeness (or not) to key safety-related events. Hence, the approach is in- terested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. for an important process such as peak pressure in a pipe) is exceeded under certain conditions. The initial development of RAVEN has been focused on providing dynamic risk assess- ment capability to RELAP-7, currently under develop-ment at the INL and, likely, future replacement of the RELAP5-3D code. Most the capabilities that have been implemented having RELAP-7 as principal focus are easily deployable for other system codes. For this reason, several side activates are currently ongoing for coupling RAVEN with soft- ware such as RELAP5-3D, etc. The aim of this document is the explaination of the input requirements, focalizing on the input structure.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

RAVEN User Manual, Revision 4

RAVEN is a generic software framework to perform parametric and probabilistic analysis based on the response of complex system codes. The initial development was aimed to provide dynamic risk analysis capabilities to the Thermo-Hydraulic code RELAP-7, currently under development at the Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose probabilistic and uncertainty quantification platform, capable to agnostically communicate with any system code. This agnosticism includes providing Application Programming Interfaces (APIs). These APIs are used to allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by inputs files or via python interfaces. RAVEN is capable of investigating the system response, and investigating the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused to- ward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. The development of RAVEN has started in 2012, when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework became stronger. RAVEN principal assignment is to provide the necessary software and algorithms in order to employ the concept developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just the individuation of the frequency of an event potentially leading to a system failure, but the closeness (or not) to key safety-related events. Hence, the approach is interested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. for an important process such as peak pressure in a pipe) is exceeded under certain conditions. The initial development of RAVEN has been focused on providing dynamic risk assess- ment capability to RELAP-7, currently under development at the INL and, likely, future replacement of the RELAP5-3D code. Most the capabilities that have been implemented having RELAP-7 as principal focus are easily deployable for other system codes. For this reason, several side activates are currently ongoing for coupling RAVEN with soft- ware such as RELAP5-3D, etc. The aim of this document is the explanation of the input requirements, focalizing on the input structure.

97 MATHEMATICS AND COMPUTING↗