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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Low temperature ethanol steam reforming: Selectivity control with lithium doping of Pt/m-ZrO 2

Lithium promoted 2%Pt/m-ZrO 2 catalysts previously observed to exhibit higher rates for the low temperature water-gas shift (LTS) were tested for the ethanol steam reforming with the aim of exploring the potential tuning of the selectivity. Characterization of catalysts having optimized Li content (0.5–0.75%Li) for LTS exhibited (a) weakened C—H bonding of formate, a proposed intermediate in the LTS mechanism, as shown by a shift in the ν(CH) band to lower wavenumbers, (b) a relatively low extent of blocking of Pt, as measured by the ν(CO) band intensity of Pt-CO, (c) increased basicity as measured by CO 2 temperature-programmed desorption with mass spectrometry, but not so high as to strongly inhibit CO 2 product removal, and finally (d) no evidence of electron transfer from Li to Pt. Here, for this study, the same catalysts were tested for ethanol steam reforming (ESR). Results show that Li could likewise weaken the C—C bond of the acetate intermediate, the analog of formate in LTS, and facilitate decarboxylation over decarbonylation altering the selectivity in favor of methanation. This trend was confirmed by fixed bed reaction testing, in-situ infrared spectroscopy experiments of transient ESR, and temperature-programmed ESR using MS. The Li-doped catalysts may be used to pre-reform ethanol prior to feeding to a methane steam reformer to increase the overall H 2 selectivity of the process. DRIFTS of steady state ESR revealed that deactivation occurs through losses in the Pt-support interface, thereby hindering the turnover of the acetate intermediate.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Grey-box and ANN-based building models for multistep-ahead prediction of indoor temperature to implement model predictive control

Model-based predictive control (MPC) strategies for heating, ventilation, and air-conditioning (HVAC) systems present an opportunity to lower building energy consumption and operational costs. Such approaches rely on the development of a model to precisely forecast building thermal dynamics, such as room air temperature or heating/cooling rate, and make control-related decisions. The control-oriented modeling of building energy systems should be accurate in predicting indoor conditions and present low computational complexity. These features are the key challenge of implementing advanced control methods such as MPC. Extant studies on building modeling for MPC have focused on step-ahead forecasting techniques to forecast building thermal dynamics, while multistep-ahead forecasting is essential. Moreover, machine learning model suitable in case of the domain-based engineering expertise are also not available. To this aim, we perform a comparative analysis of the grey-box model based on a resistance-capacitance (RC) thermal network and a machine learning model composed of an artificial neural network (ANN) for multistep-ahead prediction of building thermal dynamics using current and historical data. Actual experimental data obtained from the Flexible Research Platform (FRP) in Oak Ridge National Laboratory (US) are used for estimation and validation purposes. The average root mean squared error (RMSE) of the grey-box and ANN models are 0.89 °C and 1.02°C, respectively. Finally, the results indicate that the grey-box model outperforms the ANN model in the considered validation periods in terms of accuracy and prediction stability.

42 ENGINEERING↗
DOE OSTI2022

Tailoring Negative Thermal Expansion via Tunable Induced Strain in La–Fe–Si-Based Multifunctional Material

Zero thermal expansion (ZTE) composites are typically designed by combining positive (PTE) with negative thermal expansion (NTE) materials acting as compensators and have many diverse applications, including in high-precision instrumentation and biomedical devices. La(Fe 1-x ,Si x )13-based compounds display several remarkable properties, such as giant magnetocaloric effect and very large NTE at room-temperature. Both are linked via strong magnetovolume coupling, which leads to sharp magnetic and volume changes occurring simultaneously across first-order phase transitions; the abrupt nature of these changes makes them unsuitable as thermal expansion compensators. To make these materials more useful practically, the mechanisms controlling the temperature over which this transition occurs and the magnitude of contraction need to be controlled. In this work, ball-milling was used to decrease particles and crystallite sizes and increase the strain in LaFe 11.9 Mn 0.27 Si 1.29 H x alloys. Such size and strain tuning effectively broadened the temperature over which this transition occurs. The material’s NTE operational temperature window was expanded, and its peak was suppressed by up to 85%. This work demonstrates that induced strain is the key mechanism controlling these materials’ phase transitions. This allows the optimization of their thermal expansion towards room-temperature ZTE-applications.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Kinetically Controlled Formation of Semi-crystalline Conjugated Polymer Nanostructures

Conjugated polymer (CP) materials that are considered as an attractive choice for various electronic and optoelectronic applications possess highly heterogeneous complex structures that can span several orders of magnitude in length scale. This is due to the intricate coupling effects of weak secondary interactions and entropic forces that determine molecular organization in bulk polymer materials. Understanding the influence of molecular interactions on the emergence and evolution of nano- and microscale structures is of paramount importance to the guided design and development of condensed CP materials. Such understanding is even more critical for the rational design of hierarchical systems away from equilibrium, where weak molecular interactions can guide the system along competing kinetic pathways toward local energy minima and metastable architectures. In this work, we studied a promising concept for accessing various kinetically stabilized semi-crystalline CP nanostructures that are formed in the process of controlled chain-growth Kumada catalyst-transfer polymerization initiated by a small-molecule catalytic species which can exist in equilibrium between free and aggregated states. Specifically, we chose a small-molecule perylenedicarboximide (PDCI) polymerization catalytic initiator which has a strong tendency toward reversible supramolecular assembly–disassembly that can be controlled by temperature. Addition of a thiophene monomer to a solution of the PDCI initiator starts the chain-growth polymerization process, which produces distinct nanoscale semi-crystalline polythiophene structures formed under predominant kinetic control. Furthermore, we demonstrated that, depending on the reaction temperature (which affects the fine balance between the position of PDCI assembly–disassembly equilibrium, rate of polymerization, and solvent–solute interactions for the growing polythiophene chains), a range of kinetically trapped hierarchically organized semi-crystalline CP systems with substantially varying optoelectronic properties could be obtained. In addition to spectroscopic and electron microscopic studies, in order to better reveal the structural aspects of the generated polymer nanoscale systems, we carried out a series of X-ray diffraction and neutron scattering experiments which indicated complex hierarchical organization in these kinetically stabilized CP assembled materials. We expect that this in situ polymerization-based approach can lead to a general way to expand access to various semi-crystalline CP nanostructured materials with broadly tunable electronic and optical properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Controlling the Phase Transition in CsPbI 3 Nanowires

Cesium lead iodide (CsPbI 3 ) is a promising semiconductor with a suitable band gap for optoelectronic devices. CsPbI 3 has a metastable perovskite phase that undergoes a phase transition into an unfavorable nonperovskite phase in an ambient environment. This phase transition changes the optoelectronic properties of CsPbI 3 and hinders its potential for device applications. Therefore, it is of central importance to understand the kinetics of such instability and develop strategies to control and stabilize the perovskite phase. Here, we use ultralong CsPbI 3 nanowires as a model platform to investigate the phase transition kinetics. Our results depict the role of environmental stressors (moisture and temperature) in controlling the phase transition dynamics of CsPbI 3 , which can serve as guiding principles for future phase transition studies and the design of related photovoltaics. Furthermore, we demonstrate the controllability of phase propagation on individual nanowires by varying the moisture level and temperature.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A neural-network-enhanced parameter-varying framework for multi-objective model predictive control applied to buildings

Management of the electrical grid is becoming more complex due to the increased penetration of alternative energy generation technologies and a broadening diversity of electric loads. This complexity creates challenges in balancing demand and generation that can increase the potential for grid instabilities. One effective way to address this issue is to leverage previously unexploited demand flexibility through advanced control strategies. In this work, we propose an advanced control method, called adaptive neural parameter-varying model predictive control (ANPV-MPC), to control the temperature and energy consumption of a building via its Heating, Ventilation, and Air Conditioning system. ANPV-MPC combines key ideas in parameter-varying control, adaptive control, and online learning strategies to bridge the gap between computationally efficient linear model predictive control and more accurate nonlinear model predictive control. The novelty in ANPV-MPC is the use of a physics-inspired Bayesian neural network to estimate the coefficients of the parameter-varying linear control model. The Bayesian neural network additionally provides uncertainty estimates, triggering online training to capture evolving building system conditions. We show that ANPV-MPC can approximate the building system dynamics with a 28.39% higher accuracy than traditional linear model predictive control, resulting in 36.23% better control performance without increasing complexity of the optimal control problem. ANPV-MPC also adapts in real time to previously unseen conditions using online learning, further improving its performance.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Compact absorptivity measurement system for additive manufacturing

The present disclosure relates to a powdered bed fusion additive manufacturing (PBFAM) apparatus. The apparatus uses a container for holding a quantity of powdered material. The container has a bottom wall for supporting the powdered material, wherein the bottom wall is made from the same material as the powdered material. A temperature sensing subsystem is coupled to a portion of the container for detecting a temperature of the container. A laser generates an optical beam directed at the powdered material held by the container for melting the powdered material. A controller receives temperature information from the temperature sensing subsystem and determines an absorptivity of the powdered material based on the temperature information.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Crossover from 2D Ferromagnetic Insulator to Wide Band Gap Quantum Anomalous Hall Insulator in Ultrathin MnBi 2 Te 4

Intrinsic magnetic topological insulators offer low disorder and large magnetic band gaps for robust magnetic topological phases operating at higher temperatures. By controlling the layer thickness, emergent phenomena such as the quantum anomalous Hall (QAH) effect and axion insulator phases have been realized. Furthermore, these observations occur at temperatures significantly lower than the Néel temperature of bulk MnBi 2 Te 4 , and measurement of the magnetic energy gap at the Dirac point in ultrathin MnBi 2 Te 4 has yet to be achieved. Critical to achieving the promise of this system is a direct measurement of the layer-dependent energy gap and verification of a temperature-dependent topological phase transition from a large band gap QAH insulator to a gapless TI paramagnetic phase. Here we utilize temperature-dependent angle-resolved photoemission spectroscopy to study epitaxial ultrathin MnBi 2 Te 4 . We directly observe a layer-dependent crossover from a 2D ferromagnetic insulator with a band gap greater than 780 meV in one septuple layer (1 SL) to a QAH insulator with a large energy gap (>70 meV) at 8 K in 3 and 5 SL MnBi 2 Te 4 . The QAH gap is confirmed to be magnetic in origin, as it becomes gapless with increasing temperature above 8 K.

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

Uncertainty Quantification and Sensitivity Analysis of Non-Nuclear Advanced Controls Testbed Reactor Mockup

The research presented in this report describes our progress in applying stochastic methods and uncertainty quantification, parametric study, and variance-based sensitivity analysis (also known as Sobol sensitivity analysis) to a full-core model of a nuclear thermal propulsion (NTP) system simulated with Griffin, with the goal of developing a reduced order (surrogate) model which can be rapidly sampled while perturbing multiple input parameters. In this NTP system, reactivity and power feedback affect the rotation of control drums, which are controlled by a hybrid proportional, integral and derivative (PID) controller, actuated by the power demand and reactivity feedback from the numerical model. This model uses reactor kinetic feedback (mean generation time and $\beta$ from a transient Griffin simulation executed with the improved quasi-static method to provide the kinetic parameters) as inputs to functions which control the CD rotation angle. Using a number of stochastic method approaches, we developed a dual purpose training-surrogate model of the NTP system using polynomial regression. The trained model can be rapidly sampled while simultaneously perturbing various input parameters of the model, such as coefficients on the PID control, or temperature (directly affect the neutron cross section). The surrogate model delivers accurate results orders-of-magnitude faster (minutes, not days) than the base model. Once the base model has been trained, distributions of the uncertain parameters can be changed at will to investigate the effects of perturbing multiple inputs and their effect on the output. For example, coefficients used in the PID control system may vary due to some physical interference, or there may be uncertainty in the temperature of the neutron cross sections in various regions of the reactor. A distribution can be placed on these parameters and operational boundaries can be determined. The goal of this work is to support development of an advanced control system to operate CDs in a functioning NTP system.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

[2 + 2] Cycloaddition Produces Divalent Organic Color-Centers with Reduced Heterogeneity in Single-Walled Carbon Nanotubes

Organic color centers (OCCs), generated by the covalent functionalization of single-walled carbon nanotubes, have been exploited for chemical sensing, bioimaging, and quantum technologies. However, monovalent OCCs can assume at least 6 different bonding configurations on the sp 2 carbon lattice of a chiral nanotube, resulting in heterogeneous OCC photoluminescence emissions. Herein, we show that a heat-activated [2 + 2] cycloaddition reaction enables the synthesis of divalent OCCs with a reduced number of atomic bonding configurations. The chemistry occurs by simply mixing enophile molecules (e.g., methylmaleimide, maleic anhydride, and 4-cyclopentene-1,3-dione) with an ethylene glycol suspension of SWCNTs at elevated temperature (70–140 °C). Unlike monovalent OCC chemistries, we observe just three OCC emission peaks that can be assigned to the three possible bonding configurations of the divalent OCCs based on density functional theory calculations. Notably, these OCC photoluminescence peaks can be controlled by temperature to decrease the emission heterogeneity even further. Furthermore, this divalent chemistry provides a scalable way to synthesize OCCs with tightly controlled emissions for emerging applications.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2025

Uncertainty quantification and sensitivity analysis of a nuclear thermal propulsion reactor startup sequence

The research presented in this article describes progress in applying stochastic methods, uncertainty quantification, parametric studies, and variance-based sensitivity analysis (also known as Sobol sensitivity analysis) to a full-core model of a nuclear thermal propulsion (NTP) system simulated via the radiation transport code Griffin to simulate neutronics. Our goal is to develop a reduced-order (surrogate) model that can be rapidly sampled with perturbations to multiple input parameters. In this NTP system, reactivity and power feedback affect the rotation of control drums (CDs), which is itself controlled by a hybrid proportional-integral-derivative (PID) controller actuated by the power demand and reactivity feedback from the numerical model. This model uses reactor kinetic feedback (mean generation time [Λ] and effective delayed neutron fraction [ β eff ] from a transient Griffin simulation executed via Griffin’s improved quasi-static solver to provide the kinetic parameters) as inputs to functions that control the CD rotation angle. By investigating numerous stochastic approaches, we developed a dual-purpose surrogate model of the NTP system, using polynomial regression in the Multiphysics Object-Oriented Simulation Environment (MOOSE) Stochastic Tools Module (STM). The trained model can be rapidly sampled while simultaneously perturbing various input parameters, such as coefficients on the PID control or temperature (directly affecting the neutron cross section). The surrogate model delivers accurate (within 5%) results at speeds orders of magnitude faster (minutes, not days of computational time) than the base model. Once the surrogate model has been trained, distributions of the uncertain parameters can be changed at will to investigate the effects of perturbing multiple inputs as well as the effects of these inputs on the model output. For example, coefficients used in the PID control system may vary due to some type of physical interference, or uncertainty may exist in the temperature of the neutron cross sections in various regions of the reactor. A distribution can be placed on these parameters, and operational boundaries can be determined. The goal of this work is to support development of an advanced control system for operating CDs in a functioning NTP system. This work is a scoping study of the MOOSE STM.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
DOE OSTI2023

Physics-informed machine-learning model of temperature evolution under solid phase processes

We model temperature dynamics during Shear Assisted Proccess Extrusion (ShAPE), a solid phase process that plasticizes feedstock with a rotating tool and subsequently extrudes it into a consolidated tube, rod, or wire. Control of temperature is critical during ShAPE processing to avoid liquefaction, ensure smooth extrusion, and develop desired material properties in the extruded products. Accurate modeling of the complicated thermo-mechanical feedbacks between process inputs, material temperature, and heat generation presents a significant barrier to predictive modeling and process design. In particular, connecting micro-structural scale mechanisms of heat generation to macro-scale predictions of temperature can become computationally intractable. In this work we use a neural network (NN) model of heat generation to bridge this gap, by combining it with a simplified model of the temperature dynamics due to conduction and convection to capture the macro scale evolution of temperature. We inform the construction of the NN heat generation model using crystal plasticity simulations at the micro-structural scale to model the effects of process inputs on generation of heat. We achieved close fits of the temperature dynamics model to a diverse experimental data-set. Further, the relationships learned by the NN model between process inputs and heat generation showed qualitative agreement with those predicted by crystal plasticity simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Temperatures of Anvil Clouds and Radiative Tropopause in a Wide Array of Cloud-Resolving Simulations

Here, we present 123 cloud-resolving simulations to study how temperatures of anvil clouds and radiative tropopause (RT) change with surface warming. Our simulation results show that the RT warms at approximately the same rate as anvil clouds. This relationship persists across a variety of modeling choices, including surface temperature, greenhouse gas concentration, and the representation of radiative transfer. We further show that the shifting ozone profile associated with climate warming may give rise to a fixed RT temperature as well as a fixed anvil temperature. This result points to the importance of faithful treatment of ozone in simulating clouds and climate change; the robust anvil–RT relationship may also provide alternative ways to understand what controls anvil temperature.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Ultrafast lattice disordering can be accelerated by electronic collisional forces

In the prevalent picture of ultrafast structural phase transitions, the atomic motion occurs in a slowly varying potential energy surface determined adiabatically by the fast electrons. However, this ignores non-conservative forces caused by electron-lattice collisions, which can significantly influence atomic motion. Most ultrafast techniques only probe the average structure and are less sensitive to random displacements, and therefore do not detect the role played by non-conservative forces in phase transitions. Here we show that the lattice dynamics of the prototypical insulator-to-metal transition of VO 2 cannot be described by a potential energy alone. We use the sample temperature to control the preexisting lattice disorder before ultrafast photoexcitation across the phase transition and our ultrafast diffuse scattering experiments show that the fluctuations characteristic of the rutile metal develop equally fast (120 fs) at initial temperatures of 100 K and 300 K. This indicates that additional non-conservative forces are responsible for the increased lattice disorder. In conclusion, these results highlight the need for more sophisticated descriptions of ultrafast phenomena beyond the Born-Oppenheimer approximation as well as ultrafast probes of spatial fluctuations beyond the average unit cell measured by diffraction.

36 MATERIALS SCIENCE↗
DOE OSTI2022

An Innovative Zero-Liquid Discharge Intermediate-Cold-Liquid Eutectic-Freeze Desalination System

A novel freeze desalination technology is presented for purification of water produced from various industrial processes, including oil and gas extraction. The main innovative idea is introducing a water-immiscible intermediate-cold-liquid (ICL) medium which absorbs the cold from a refrigeration cycle and transfers it to a freezing chamber where it is mixed with brine. The cold ICL enters the freezing chamber at temperatures between -10°C to -30°C. Precooled brine in injected into the freezing chamber and pure water freezes out of the brine upon mixing with the ICL. The rate of freezing inside the chamber is controlled by adjusting the flow rate of the cold ICL relative to the inlet brine. Both eutectic freeze and non-eutectic freezing are possible by controlling the temperature of the freezing chamber. The solid-liquid slurry created in the freezing chamber flows to a filter separation unit after passing through a separation column. The ice crystals are separated from the liquid by a rotary filter device. The collected ice is rinsed by a centrifugal unit. The rinsed ice is melted to produce fresh water. The developed technology resolves the issues common to current freeze-desalination systems while offering superior heat transfer performance ascribed to direct-contact between the cooling medium and the brine in addition to producing solid salt and pure water. The system operates under atmospheric pressure. The low operation temperature minimizes the corrosion issues common in systems exposed to brine. Also, due to the low operating temperature, there are no concerns about evaporation of volatile organics from the produced water during the treatment. The developed technology effectively eliminates the use of chemicals for pretreatment of brine. The system can be constructed from commercially available components. Extensive testing using a lab-scale prototype by using synthetic brine and actual produced water with TDS values from 50,000 ppm to 300,000 ppm showed that freshwater could be achieved with relatively small dependency on the quality of the input brine. The quality of the treated water was found to be mainly a function of the duration of the centrifugal process. Treated water with TDS as low as 200 ppm could be achieved in the tests. Thermo-economic modeling coupled with system level thermochemical process model were developed for prediction of the LCOW. The LCOW was predicted to be $\$$0.50 to $\$$0.90 per barrel of produced water. The potential markets for deployment of the technology are the oil and gas, in-land thermoelectric powerplants, RO desalination plants, and mining industry.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Data-Driven Linear Parameter-Varying Modeling and Control of Flexible Loads for Grid Services

Flexible loads have great potential to improve the electric grid's flexibility and stability. To effectively control large ensembles of heterogeneous loads, reliable models thereof are required. This paper presents a data-driven modeling and control approach to manage flexible loads for providing grid services. We leverage a linear parameter-varying autoregressive moving average (LPV-ARMA) model to describe the aggregate load response, where the parameters in the model are used to capture external environmental impacts (e.g., weather). A gain-scheduling feedback controller is then developed to adapt to environmental variations. This data-driven approach can be easily applied to different types of loads in various environmental conditions. In addition to the ensemble controller, distributed load controllers are designed to deliver grid services, while maintaining the quality of service of inherent load tasks. We demonstrate the work on the IEEE 37-node distribution system for real-time power regulation services through control of thermostatically controlled loads.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2021

Forced Crowding of Colloids by Thermophoresis and Convection in a Custom Liquid Clusius–Dickel Microdevice

Here, we report a study demonstrating that simultaneous induction of a steady-state convection current and temperature gradient in a confined geometry can be an effective way to force crowding of dissolved particulates. To investigate this thermogravitationally driven concentration of particles in-situ, we developed a micro-device capable of sustaining controlled transverse temperature gradients within a 5 cm long, 0.1 mm inner diameter capillary that allowed visualization of particle movement with standard optical microscopy. Experiments were conducted on two material systems representative of nano-scale small molecules and micro-scale particles. With the small molecules (aromatic dyes, 530-790 g/mol, 1-1.5 nm) thermophoretic and gravitational effects in the micro-device resulted in an asymmetrical 2x concentration change along the capillary height over 3 days. In contrast, the concentration change under similar conditions for 40-micron diameter latex colloids is 50-fold in 30 minutes. This dramatic difference in separation times is consistent with simulations and models of thermophoresis where the thermophoretic effect scales with particle size. Induced crowding of particulates leads to formation of accumulation and depletion zones in at the bottom and top of the capillary respectively. Both the concentration of dye molecules over time in the depletion zone and spatial distribution of colloids over the entire capillary length were found to be good fits to simple first-order exponential decay functions. Furthermore, these results suggest potential applications of thermogravitational separation in developing new functional materials via thermophoretic and convective effects.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Ring Model Development and Validation for Prismatic HTGR Core Thermal-hydraulics and Safety Analysis

Because of the complex core geometry, prismatic high temperature gas-cooled reactors (prismatic HTGRs) often exhibit complex thermal fluid behaviors during both normal operating and transient conditions. Most HTGR designs rely on passive safety system for decay heat removal, such as the reactor cavity cooling system (RCCS). During postulated accidents like Pressurized Conduction Cooldown (PCC) event, the decay heat is first radially transferred from the core region to the reactor vessel outer surface, then to the RCCS cooling panels. The peak fuel temperature is controlled by heat transfer mechanisms with two distinctive characteristic length scales, i.e., the core-wise effective heat conduction and local heat conduction in the fuel pellet scale. As both length scales are essential to determine the fuel temperature, from the modeling perspective, computer codes must be able to capture heat transfer in both scales. This is challenging for both computational fluid dynamics (CFD) tools because of extremely large amount of computation resources required, and for system analysis codes because of the challenge to model the complex core geometry. Under the support of DOE-NE’s Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, efforts have been pursued to support HTGR technology development and its modeling and simulation needs. There is a particular need for advanced modeling and simulation tools to predict thermal-fluid behavior during safety-related transients. In our previous studies, the ring model was adopted in SAM and further developed to simulate the normal operating condition and a PCC event using the MHTGR-350 design of General Atomics as the reference design. This current work represents a continuation of these previous efforts, and the focus is to critically review and examine simplifications and assumptions made to develop the ring model, and to perform code validation using experimental data from an integral-effect test facility, the High Temperature Test Facility (HTTF) at the Oregon State University. In this study, the test PG-27 from the HTTF test suite was selected for code validation purpose. The test PG-27 is a transient test designed to simulate the PCC event of the MHTGR design. Very good agreements between SAM prediction and experimental measurements were found in both coolant and solid structure temperatures during the transient.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗