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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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At least 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Insights from modeling and observational evaluation of a precipitating continental cumulus event observed during the MC3E field campaign

A case of shallow cumulus and precipitating cumulus congestus sampled at the Atmospheric Radiation Measurement (ARM) Program Southern Great Plains (SGP) supersite is analyzed using a multi-sensor observational approach and numerical simulation. Observations from a new radar suite surrounding the facility are used to characterize the evolving statistical behavior of the precipitating cloud system. This is accomplished using distributions of different measures of cloud geometry and precipitation properties. Large-eddy simulation (LES) with size-resolved (bin) microphysics is employed to determine the forcings most important in producing the salient aspects of the cloud system captured in the radar observations. Our emphasis is on assessing the importance of time-varying vs. steady-state large-scale forcing on the model's ability to reproduce the evolutionary behavior of the cloud system. Additional consideration is given to how the characteristic spatial scale and homogeneity of the forcing imposed on the simulation influences the evolution of cloud system properties. Results indicate that several new scanning radar estimates such as distributions of cloud top are useful to differentiate the value of time-varying (or at least temporally well-matched) forcing on LES solution fidelity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Designing a GIS-based supply chain for producing carinata-based sustainable aviation fuel in Georgia, USA

Carinata is a potential crop for sustainable aviation fuel (SAF) production in the southern USA. However, as a novel crop, the cost-effectiveness and environmental feasibility of carinata feedstock are unknown, and there are questions about the optimal supply chain configuration for carinata-based SAF production. This study aims to design a supply chain model for carinata-based SAF production by optimizing the location of farms and facilities (e.g. storage units, crushing mills, biorefineries) for a minimum transportation cost under a set of supply and demand conditions. An integrated mixed-integer linear programming (MILP) model was combined with geographical information system (GIS) analysis to design a spatially explicit supply chain configuration. The GIS-based network analysis considered all of the counties in Georgia to set the candidate locations of carinata farms and facilities, and determined minimum cost and emission routes between those counties and the airport using existing transportation networks and modes (e.g. road, rail and pipeline). The MILP model determined the final selection of the farms and the number of facilities and their locations over those minimum-cost routes. With this supply chain configuration, the minimum price of SAF was $\$$0.92 L –1 , which is $\$$0.44 higher than conventional aviation fuel (CAF). The associated carbon intensity of SAF was estimated at 940.7 g CO 2 e L –1 , a reduction of 66% relative to the carbon intensity of equivalent CAF. The study found that a carbon tax (or subsidy) of $\$$230.48 t CO 2 e –1 would be needed to overcome the cost differential with CAF and promote carinata-based SAF in Georgia.

09 BIOMASS FUELS↗
DOE OSTI2018

Experimental Study of Beam Dynamics in the PIP-II MEBT Prototype

The Proton Improvement Plan, Stage Two (PIP-II) [1] is a program of upgrades proposed for the Fermilab injection complex, which central part is an 800 MeV, 2 mA CW SRF linac. A prototype of the PIP-II linac front end called PIP-II Injector Test (PIP2IT) is being built at Fermilab. As of now, a 15 mA DC, 30-keV H- ion source, a 2 m-long Low Energy Beam Transport (LEBT), a 2.1 MeV CW RFQ, followed by a 10 m Medium Energy Beam Transport (MEBT) have been assembled and commissioned. The MEBT bunch-by-bunch chopping system and the requirement of a low uncontrolled beam loss put stringent limitations on the beam envelope and its variation. Measurements of transverse and longitudinal beam dynamics in the MEBT were performed in the range of 1- 10 mA of the RFQ beam current. Almost all measurements are made with 10 μs beam pulses in order to avoid damage to the beam line. This report presents measurements of the transverse optics with differential trajectories, reconstruction of the beam envelope with scrapers and an Allison emittance scanner, as well as bunch length measurements with a Fast Faraday Cup.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Reactivity Coefficient Measurements and Sensitivity Studies [Abstract]

Nuclear data validation is often performed today using criticality measurements. The gold standard for criticality measurements is the International Criticality Safety Benchmark Experiment Project (ICSBEP). The validation specifically focuses on the effective multiplication factor (k eff ). K eff is a relatively easy parameter to infer and has reduced uncertainty due to being at or above critical. However, while k eff is a well-documented parameter with detailed sensitivity and uncertainty analysis, it cannot be used as a standalone metric to determine inaccuracies in nuclear data (e.g., cross section data, PFNS, nu), which is based on theory, physics, and differential measurements. The Experiments Underpinned by Computational Learning for Improvements in nuclear Data (EUCLID) project aims to identify compensating errors in specific nuclear data by optimally designing experiments that are sensitive to a suite of measurement parameters beyond k eff . By identifying how each parameter's nuclear data sensitivity differs from others, experiments can be designed to constrain questionable nuclear data. One sensitivity that is of particular interest to this project includes reactivity coefficient sensitivities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Schema Elements for Granta Annual Report: FY2024

Granta: Materials Intelligence (Granta: MI) is a commercial database software distributed by Ansys, Inc. that is utilized by the Nuclear Security Enterprise (NSE) to organize and store relevant materials data. Lack of standard and well-documented database schema is the primary obstacle to an NSE materials data management solution, so the objective of this project is to create and document such a schema. In FY21, an approach for designing, documenting, and managing a standard database schema was described based on the creation of schema elements (collections of attributes used to describe particular aspects of the data) to be used as building blocks for creating various database tables without duplication. In FY22, these methods were applied through a multi-site collaboration to create and document the schema elements necessary to build a thermogravimetric analysis (TGA) testing table. In FY23 the schema was expanded to include elements for a differential scanning calorimetry (DSC) table, along with schema for supporting metadata tables including Instruments, Projects, Documents, and Testing Series. In FY24 the following progress was made, again through multi-site collaboration: • The existing schema elements were modified to accommodate thermomechanical analysis (TMA) data, and a table, Test Data: TMA, was created for managing TMA data. • The elements necessary for the following additive manufacturing (AM) data tables (directed at data specific to selective laser sintering AM technology) were created: • AM Builds • AM Processes • AM Part Designs • Built AM Parts • AM Feedstock Materials • AM Feedstock Material Batches • The elements necessary for creating a Calibrated Material Models table were created, and the Calibrated Material Models table was created. In FY25 the existing schema will be deployed on the production enterprise Granta instance on the enterprise secure network. Schema elements will be appended, and new elements created as necessary, to allow the creation of tables specifically to support materials testing, AM process development, and design and analysis for modernization programs.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Suramin blocks interaction between human FGF1 and FGFR2 D2 domain and reduces downstream signaling activity

The extracellular portion of the human fibroblast growth factor receptor2 D2 domain (FGFR2 D2) interacts with human fibroblast growth factor 1 (hFGF1) to activate a downstream signaling cascade that ultimately affects mitosis and differentiation. Suramin is an antiparasiticdrug and a potent inhibitor of FGF-induced angiogenesis. Suramin has been shown to bind to hFGF1, and might block the interaction between hFGF1 and FGFR2 D2. Here, we titrated hFGF1 with FGFR2 D2 and suramin to elucidate their interactions using the detection of NMR. The docking results of both hFGF1-FGFR2 D2 domain and hFGF1-suramin complex were superimposed. The results indicate that suramin blocks the interaction between hFGF1 and FGFR2 D2. We used the PyMOL software to show the hydrophobic interaction of hFGF1-suramin. In addition, we used a Water-soluble Tetrazolium salts assay (WST1) to assess hFGF1 bioactivity. The results will be useful for the development of new antimitogenic activity drugs. - Highlights: • The interfacial residues on hFGF1-FGFR2 D2 and hFGF1-Suramin contact surface were mapped by {sup 1}H-{sup 15}N HSQC experiments. • hFGF1-FGFR2 D2 and hFGF1-Suramin complex models were generated from NMR restraints by using HADDOCK program. • We analyzed hFGF1-Suramin complex models and found the interaction between hFGF1-Suramin is hydrophobic. • The bioactivity of the hFGF1-FGFR2 D2 and hFGF1-Suramin complex was studied by using WST1 assay.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Fuel Property Characterization and Prediction

The Fuel Property Characterization and Prediction effect with the Co-Optimization of Fuels and Engines (Co-Optima) initiative is focused on the measurement or prediction of key fuel properties. Efforts under the Vehicle Technologies Office (VTO) have focused on understanding how fuel evaporation phenomena can impact auto-ignition, mixing, and pollutant formation by studying the most important azeotropic interactions between alcohols and gasoline components utilizing a novel Differential Scanning Calorimetry/Thermogravimetric Analysis instrument coupled to a Mass Spectrometer (DSC/TGA/MS). In another effort, Nuclear Magnetic Resonance (NMR) measurements were utilized to understand how oxygenate clusters and networks contribute to vapor pressure effects. This is critical for predicting fuel droplet evaporation and effects on combustion. A method was developed on the Advanced Fuel Ignition Delay Analyzer (AFIDA) to rapidly measure the Research Octane Number (RON) and Sensitivity (S) of samples utilizing small volumes, which will aid in the pace of development of new fuels having targeted properties. Additionally, the AFIDA was employed to develop a method for fast measurement of phi-sensitivity in a bench-scale test that will allow studies of molecular structure effects on mechanism kinetics of this poorly understood kinetic phenomena. A flow reactor was also utilized to investigate the mechanistic basis of phi-sensitivity by identifying and quantitating key species in autoignition mechanisms that can explain phi-sensitivity. The outcomes of these efforts will help ensure that the Co-Optima program can advance the underlying science needed to develop biomass-derived-fuel and advanced engine technologies that will work in tandem to achieve Co-Optima's efficiency, environmental, and economic goals.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2016

Automated property optimization via ab initio O(N) elongation method: Application to (hyper-)polarizability in DNA

An automated property optimization method was developed based on the ab initio O(N) elongation (ELG) method and applied to the optimization of nonlinear optical (NLO) properties in DNA as a first test. The ELG method mimics a polymerization reaction on a computer, and the reaction terminal of a starting cluster is attacked by monomers sequentially to elongate the electronic structure of the system by solving in each step a limited space including the terminal (localized molecular orbitals at the terminal) and monomer. The ELG-finite field (ELG-FF) method for calculating (hyper-)polarizabilities was used as the engine program of the optimization method, and it was found to show linear scaling efficiency while maintaining high computational accuracy for a random sequenced DNA model. Furthermore, the self-consistent field convergence was significantly improved by using the ELG-FF method compared with a conventional method, and it can lead to more feasible NLO property values in the FF treatment. The automated optimization method successfully chose an appropriate base pair from four base pairs (A, T, G, and C) for each elongation step according to an evaluation function. From test optimizations for the first order hyper-polarizability (β) in DNA, a substantial difference was observed depending on optimization conditions between “choose-maximum” (choose a base pair giving the maximum β for each step) and “choose-minimum” (choose a base pair giving the minimum β). In contrast, there was an ambiguous difference between these conditions for optimizing the second order hyper-polarizability (γ) because of the small absolute value of γ and the limitation of numerical differential calculations in the FF method. It can be concluded that the ab initio level property optimization method introduced here can be an effective step towards an advanced computer aided material design method as long as the numerical limitation of the FF method is taken into account.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Generating tissue topology through remodeling of cell-cell adhesions

During tissue morphogenesis, cellular rearrangements give rise to a large variety of three-dimensional structures. Final tissue architecture varies greatly across organs, and many develop to include combinations of folds, tubes, and branched networks. To achieve these different tissue geometries, constituent cells must follow different programs that dictate changes in shape and/or migratory behavior. One essential component of these changes is the remodeling of cell-cell adhesions. Invasive migratory behavior and separation between tissues require localized breakdown of cadherin-mediated adhesions. Conversely, tissue folding and fusion require the formation and reinforcement of cell-cell adhesions. Cell-cell adhesion plays a critical role in tissue morphogenesis; its manipulation may therefore prove to be invaluable in generating complex topologies ex vivo. Recapitulating these shapes in engineered tissues would enable a better understanding of how these processes occur in vivo, and may lead to improved design of organs for clinical applications. In this review, we discuss work investigating the formation of folds, tubes, and branched networks with an emphasis on known or possible roles for cell-cell adhesion. We then examine recently developed tools that could be adapted to manipulate cell-cell adhesion in engineered tissues. - Highlights: • Cell shape changes and rearrangements give rise to highly varied tissue topology. • Generating 3D structures requires differential remodeling of cell-cell adhesions. • Cell-cell adhesion could be experimentally manipulated to engineer tissues.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Importance of Higher Fidelity Model Geometries during Optimization of Critical Experiments

PARADIGM, PARallel Approach of Differential and InteGral Measurements, is a cross-collaborative effort at Los Alamos National Laboratory between nuclear data theorists, differential and integral experimenters, as well as machine learning statisticians to tackle uncertainties in the intermediate region of 239 Pu. In essence, the idea behind PARADIGM is to remove the linear conceptualization of the nuclear data pipeline, shown in Figure 1, and replace it with a far more parallelized approach. The novel approach leverages machine learning to guide which differential measurements and integral experiments will result in the largest decrease in uncertain ties for a nuclide reaction pair in a given energy range. The concept builds off earlier work, EUCLID, which focused on the fast region of 239 Pu. The practical benefit of having evaluation, differential measurement, and integral experiment personnel in collaboration with machine learning is to represent the entire nuclear data in one snapshot. This enable large reduction in the time to deliver improved nuclear data, which using the PARADIGM approach could be done in 3 years. A general outline of PARADIGM and specific topics are available in other papers. The discussion here will pertain directly to the integral experiment design. More specifically, the process of taking a rough design and transforming it into a finalized neutronic model will be discussed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Developments and Challenges in the Design of the ITER DRGA

We report the ITER Diagnostic Residual Gas Analyzer (DRGA) will measure the distribution of gas species, i.e., deuterium (D), tritium (T), and impurities, in the divertor exhaust stream and in the plasma periphery, with time resolution relevant to fusion plasma–wall particle dynamics. The uniqueness of the DRGA, over previous implementations of plasma dynamics residual gas analysis, is an integrated approach, combining mass and low-temperature plasma-activated optical spectroscopy, in a differentially pumped analysis station. A further unique feature of the ITER divertor-specific DRGA is an ~8-m separation of the analysis station from the sampled pumping duct, while still maintaining a ~1-s response time for hydrogen isotopic concentrations. ITER DRGA final design activities are strongly benefiting from testing of prototypical DRGA components and methods on present fusion devices, most currently on JET and W7-X. DRGA systems are implemented on both these devices and include sensors (and pumping methods) that are directly relevant to the ITER DRGA design. The recent JET-DTE2 campaign has provided the first experience on operating the combined ITER DRGA sensors with D-T plasmas. While enhancing system design for ITER, this experience on operating devices has also revealed additional engineering challenges, which further guide the continuing final design project. Meanwhile, the recent determination that the ITER DRGA, with slight optimization, will resolve the helium isotopes well enough to support an ITER pre-DT, He-3-based heating scheme, has greatly increased ITER Research Program interest in the DRGA and its implementation well ahead of the DT phase.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Methodology for Generating Covariance Data of Thermal Neutron Scattering Cross Sections

This paper details and implements a framework for evaluating thermal neutron scattering cross sections that provide S(α,β) data and covariance data for hydrogen in light water. This methodology involves perturbing model parameters of molecular dynamics potentials and fitting the simulation results to experimental data. The framework is general and can be applied to any material or simulation method. The fit is made using the Unified Monte Carlo method to experimentally measure double-differential scattering cross sections of light water at the Spallation Neutron Source at Oak Ridge National Laboratory. Mean values and covariance data were generated for model parameters, phonon density of states, double-differential cross sections, and total scattering cross sections. These posterior parameter values were very similar to their prior values with a maximum relative error of 0.54%. This falls within in the Unified Monte Carlo–calculated uncertainties on the order of 2.7%. Additionally, posterior double-differential cross sections agree favorably with ENDF/B-VIII.0 cross sections. The new thermal scattering law was tested by comparing it against benchmarks from the International Criticality Safety Benchmark Evaluation Project Handbook, which showed a slight improvement over the ENDF/B-VIII.0 library. Additionally, the covariance matrix of the phonon density of states was validated to confirm that the spread of k eff from the density of states used to generate the covariance matrix was similar to the spread of k eff from the density of states of the sampled covariance matrix.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2011

Regularity and Variationality of Solutions to Hamilton-Jacobi Equations. Part II: Variationality, Existence, Uniqueness

We formulate an Hamilton-Jacobi partial differential equation H(x, Du(x))=0 on a n dimensional manifold M, with assumptions of convexity of the sets {l_brace}p:H(x,p){<=}0) is a subset of T{sub x}*M, for all x.We reduce the above problem to a simpler problem; this shows that u may be built using an asymmetric distance (this is a generalization of the 'distance function' in Finsler geometry); this brings forth a 'completeness' condition, and a Hopf-Rinow theorem adapted to Hamilton-Jacobi problems. The 'completeness' condition implies that u is the unique viscosity solution to the above problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Pentamidine blocks the interaction between mutant S100A5 and RAGE V domain and inhibits the RAGE signaling pathway

The human S100 protein family contains small, dimeric and acidic proteins that contain two EF-hand motifs and bind calcium. When S100A5 binds calcium, its conformation changes and promotes interaction with the target protein. The extracellular domain of RAGE (Receptor of Advanced Glycation End products) contain three domains: C1, C2 and V. The RAGE V domain is the target protein of S100A5 that promotes cell survival, growth and differentiation by activating several signaling pathways. Pentamidine is an apoptotic and antiparasitic drug that is used to treat or prevent pneumonia. Here, we found that pentamidine interacts with S100A5 using HSQC titration. We elucidated the interactions of S100A5 with RAGE V domain and pentamidine using fluorescence and NMR spectroscopy. We generated two binary models—the S100A5-RAGE V domain and S100A5-Pentamidine complex—and then observed that the pentamidine and RAGE V domain share a similar binding region in mS100A5. We also used the WST-1 assay to investigate the bioactivity of S100A5, RAGE V domain and pentamidine. These results indicated that pentamidine blocks the binding between S100A5 and RAGE V domain. This finding is useful for the development of new anti-proliferation drugs. - Highlights: • The interaction between mS100A5–RAGE V was investigated by fluorescence spectroscopy. • The interfacial residues on mS100A5–RAGE V and mS100A5–pentamidine contact surface were mapped by {sup 1}H-{sup 15}N HSQC experiments. • mS100A5–RAGE V and mS100A5–pentamidine complex models were generated from NMR restraints using HADDOCK program. • The bioactivity of the mS100A5–RAGE V and mS100A5–pentamidine complex was studied using WST-1 assay.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Effect of Cooling Rate on Microsegregation During Solidification of Superalloy INCONEL 718 Under Slow-Cooled Conditions

The solidification sequence, microstructural evolution, solid-liquid interface variation, interdendritic segregation, and elemental distribution of as-cast IN718 alloy at three slow-cooling rates (5, 10, and 20 °C/min) were investigated by differential scanning calorimetry (DSC), confocal laser scanning microscopy (CLSM), optical microscopy (OM), field-emission scanning electron microscopy (FESEM), and electron-probe microanalysis (EPMA) techniques. The results indicate that as the cooling rate decreases, the constitutional supercooling at the solidification front affects the solid-liquid interface more significantly, and the size and quantity of the Laves phase increase. However, the composition of the Laves phase is insensitive to the cooling rate in the range of conditions studied here. In dendrite core, the contents of Ni, Cr, Fe, and Al follow a slight downward trend with an increasing cooling rate, whereas the Nb, Mo, and Ti contents show an upward trend. Additionally, Mo shows a stronger propensity to segregate under slow-cooling conditions because its effective partition coefficient almost linearly decreased with decreasing cooling rate, which is same as Nb. Using the parameters experimentally determined in this study and the Clyne–Kurz equation, we achieved reasonable agreement between the calculated and measured liquid composition change during the solidification process at different cooling rates. All experimental and theoretical programs in this research were undertaken with the aim of gaining further understanding of the microsegregation behaviors in large-scale IN718 ingots, whose cooling rates are within a lower range.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Artificial Neural Network Representation of Dynamic Criticality Excursion Experiment Data

This paper outlines time dependent considerations in applying the artificial neural network (ANN) paradigm to dynamic nuclear criticality excursion experiment data. The ANN amongst a variety of uses, can apply to function fitting of multiple input-multiple output data sets, pattern recognition, cluster analysis, and time series prediction. The ANN is a component of Machine Learning (ML) data analysis. The practical application of ML to criticality safety has been funded through the Y-12 Plant Directed Research and Demonstration (PDRD) program to support criticality safety and emergency planning efforts. The initial effort considered function fitting of criticality excursion data. Recent efforts successfully extended the ANN to criticality safety code validation. The initial ANN work applied to criticality excursions centered around multivariate function fitting of static (tabular) data taken from the French Consequences Radiologiques d'un Accident de Criticite (CRAC) and SILENE reactor campaigns. The former report (LeCorche) summarized important tabular data for the first 44 CRAC experiments. The initial ANN was trained to specific time-independent inputs, then applied to predict peak fission rate and total fissions for a wide variety of fissile solutions process conditions. This work extends that effort to apply the ANN paradigm to dynamic prediction of time-dependent criticality excursion experiment data. A dynamic criticality excursion in fissile solution represents a complex multiphysics phenomenon. Factors include among other things, coupled neutronic, multiphase fluid flow, temperature, pressure, acoustic shock, boiling heat transfer, reactivity feedback, and radiolytic gas considerations. There have been many efforts to model (over time) as coupled ordinary and (space) as partial differential equations (ODE, PDE). The ANN representation noted here does not rely on solving explicit ODE or PDEs. Rather the intent is to demonstrate that as a time series, the criticality excursion can be predicted by training specific ANN architectures to experimental input and output time series exclusively. The rationale for extending this work to the time-dependent regime is important in developing a 'criticality excursion emulator' solely on the basis of learning characteristics from experiment dynamic data. An emulator is different than a computer code simulator driven by the solution of ODEs and PDEs given select 'tuning parameters' that match outputs to experiments. A criticality excursion emulator, derived exclusively from an ANN or ML, could predict most likely excursion characteristics, trained from historic dynamic experiment data, presented with input values within the range of those experiments. The work noted shows that generic time-series ANN architectures can be satisfactorily applied to the prediction of dynamic, extremely chaotic criticality excursion power given an initial time dependent input series. In reality, an excursion emulator could be driven by user-supplied initial (t=0), time independent or constant values to generate time dependent outputs. Thus ongoing work includes developing a custom ANN such that initial dynamic conditions would be predicted. Those results then serve as inputs to a customized ANN architecture currently in development. (author)

42 ENGINEERING↗
DOE OSTI2015

Nuclear heating measurements by in-pile calorimetry: prospective works for a microsensor design

Since 2009 works have been performed in the framework of joint research programs between CEA and Aix-Marseille University. The main aim of these programs is to design and develop in-pile instrumentations, advanced calibration procedure and accurate measurement methods in particular for the new Material Testing Reactor (MTR) under construction in the South of France: Jules Horowitz Reactor (JHR). One major sensor is a specific radiometric calorimeter, which was studied out-of-pile from a thermal point of view and in-pile during irradiation campaigns. This sensor type is dedicated to measurements of nuclear heating (energy deposition rate per mass unit induced by interactions between nuclear rays and matter) inside experimental channels of MTRs. This kind of in-pile calorimeter corresponds to heat flux calorimeter exchanging with the external cooling fluid. This thermal running mode allows the establishment of steady thermal conditions inside the sensor to carry out online continuous measurements inside the reactor (core or reflector). Two main types of calorimeters exist. The first type consists of a single cell calorimeter. It is divided into a sample of material to be tested and a jacket instrumented with two thermocouples or a single thermocouple (Gamma Thermometer). The second, called a differential calorimeter, is composed of two superposed twin cells (a measurement cell containing a sample of material, and a reference cell to remove the heating of the cell body) instrumented with four thermocouples and two electrical heaters. Contrary to a single-cell calorimeter, a differential calorimeter allows the compensation of the parasite nuclear heating of the sensor body or jacket. Moreover, it possesses interesting advantages: thanks to the heaters embedded in the cells, three different measurement methods can be applied during irradiations to quantify nuclear heating. The first one is based on the use of out-of-pile calibration curves obtained by generating a heat source by the Joule Effect inside each calorimetric cell. The second one is a zero method consisting in cancelling the difference in cell responses with an additional energy into the reference cell. The last measurement method is based on current additions in the two calorimetric cells. However, one drawback of the existing differential calorimeter is the size of the sensor: a great length equal to 220 mm and a diameter equal to 18 mm. This current size leads to measurement limitations. This paper will begin with a presentation of these measurement limitations from a bibliographic state. Each limitation will be detailed and in particular in the case of a high nuclear heating level expected, for instance, inside the JHR's core at its highest nominal power. The second part of the paper will develop the scientific skills of each partner in heat sciences, micro technology and nuclear physics necessary to design a new calorimetric micro-system: the advantages of studied microelements such as micro-thermocouples, micro- fluxmeters and micro-heaters will be presented. The last part will discuss preliminary designs. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗