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At least 127 records · Page 7

All sources NASA NTRS DOE OSTI

DOE OSTI2020

Autonomous Navigation and Control of UGVs' in Nuclear Power Plants - 20381

The purpose of the husky A200 ground robot is to autonomously navigate through the places where it is very hazardous for human beings to reach and operate, like nuclear power plants, chemical industries. The aim is to navigate the ground robot autonomously with an Arm mounted on the robot along with the different sensors as camera, and Lidar. The autonomous motion of the robot is controlled by the controller which uses path planner for trajectory generation of the robot. The mission planner uses the current position of the husky A200, given the way points of the initial and the destination it would extract a best possible route based on the current events provided using GMapping. The global reference frame is used for planning the way points. Creating the appropriate path and the actions required to follow the path are given by the motion planner. The motion planner depends on the active sensor data such as obstacles, lanes, based on the sensor data feasible path is generated. Feasibility of the path is determined by the dynamics of the husky and a series of points generated with certain velocity and acceleration profile. The controller adjusts the lateral, longitudinal and yaw motion of the husky to command the behaviors. The kinematic model is developed for kinematic motion of the husky and the dynamic model is developed for transient and steady state characteristics. The images and other type of data captured by the camera are processed through the computational framework used to build machine learning models. TensorFlow will be used for deep learning and to identify and classify different objects around the husky. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2023

Temporally continuous thermofluidic–thermomechanical modeling framework for metal additive manufacturing

Additive manufacturing (AM) is known to generate large magnitudes of residual stresses (RS) within builds due to steep and localized thermal gradients. In the current state of commercial AM technology, manufacturers generally perform heat treatments in effort to reduce the generated RS and its detrimental effects on part distortion and in-service failure. Computational models that effectively simulate the deposition process can provide valuable insights to improve RS distributions. Accordingly, it is common to employ Computational fluid dynamics (CFD) models or finite element (FE) models. While CFD can predict geometric and thermal-fluid behavior, it cannot predict the structural response (e.g., stress–strain) behavior. On the other hand, an FE model can predict mechanical behavior, but it lacks the ability to predict geometric and fluid behavior. Thus, an effectively integrated thermofluidic–thermomechanical modeling framework that exploits the benefits of both techniques while avoiding their respective limitations can offer valuable predictive capability for AM processes. In contrast to previously published efforts, the work herein describes a one-way coupled CFD-FEA framework that abandons major simplifying assumptions, such as geometric steady-state conditions, the absence of material plasticity, and the lack of detailed RS evolution/accumulation during deposition, as well as insufficient validation of results. Here, the presented framework is demonstrated for a directed energy deposition (DED) process, and experiments are performed to validate the predicted geometry and RS profile. Both single- and double-layer stainless steel 316L builds are considered. Geometric data is acquired via 3D optical surface scans and X-ray micro-computed tomography, and residual stress is measured using neutron diffraction (ND). Comparisons between the simulations and measurements reveal that the described CFD-FEA framework is effective in capturing the coupled thermomechanical and thermofluidic behaviors of the DED process. The methodology presented is extensible to other metal AM processes, including power bed fusion and wire-feed-based AM.

42 ENGINEERING↗

DOE OSTI2025

Ultrafast dynamics of a fermion chain in a terahertz field-driven optical cavity

In this article, we study the effect of a terahertz field-driven single cavity mode for ultrafast control of a fermion chain with dissipation-induced nonlinearity and quadratic coupling to an infrared-active phonon mode. Unlike the first-order phase transition in the nonequilibrium steady state of the system without a cavity or with a high-quality cavity, we find that a realistic dissipation in a low-quality driven cavity process prevents such a transition. Without realistic photon loss from the cavity, the transition only survives for the lower phonon-polariton branch with strong drive; a weak laser field fails to induce the phase transition and renders the polaritons symmetrical. The ability to control the phase transition is crucial for realizing strongly modulated steady states; we propose experimentally feasible regimes where this occurs.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

Multi-Period Optimization of Multi-Timescale Energy Systems: Application to Solid-Oxide Electrolysis Cells

A presentation detailing the recent progress in the optimization of Solid-Oxide Cells under Chemical Degradation. The existing methods typically involve a quasi-steady state assumption to make this large-scale problem tractable. Here, we introduce an extension to this method that allows for adaptive coupling of degradation with the fast-timescale process based on a variety of error thresholds. This reduces the error accumulated during long-term optimization of the SOC under degradation and allows for decision making at multiple timescales. The results presented include long-term operating profiles of the SOC under degradation with both steady-state and fluctuating day to day operation.

Giridhar, Nishant↗

DOE OSTI2021

WaterTAP3 (The Water Technoeconomic Assessment Pipe-Parity Platform)

The Water Technoeconomic Assessment Pipe-Parity Platform (WaterTAP3) was developed under the National Alliance for Water Innovation (NAWI) to facilitate consistent technoeconomic assessments of desalination treatment trains. The WaterTAP3 is an analytically robust modeling tool that can be used to evaluate water technology cost, energy, environmental, and resiliency tradeoffs across different water sources, sectors, and scales. The model simulates steady-state water treatment train performance and costs including flow and constituent mass balance across unit processes, based on source water conditions, configurations of treatment technologies, and system-level techno-economic assumptions. Users can build a new treatment train by connecting any number of unit processes, specific for their context and system, or selecting a train from the treatment train library. The model contains various technical and cost parameter options for a range of treatment processes and a library of influent water quality characteristics for a variety of source waters and case studies. Users can customize water quality parameters to evaluate the technology performance in their context. The model can be set up for different assessment needs including simulation, optimization, and uncertainty and sensitivity analyses. The results from WaterTAP3 can help identify trade-offs among the different system performance metrics, with insight on how particular technologies or systems promote pipe-parity. The flexibility and comprehensive scope of the tool makes it a promising solution to industry-wide water technoeconomic evaluations, leading to more informed water investment decisions and technologies. As a user-friendly, open-source platform, WaterTAP3 can be used by industry, academia, policymakers, planners, and those with or without extensive analytical experience.

Miara, Ariel↗

DOE OSTI2021

Water Technoeconomic Assessment Pipe-Parity Platform (WaterTAP3)

Miara, Ariel↗

DOE OSTI2023

Development of MHD for NekRS

A spectral-element-based formulation of incompressible MHD is presented in the context of the open-source fluid-thermal code, Nek5000/RS. The formulation supports magnetic fields in a solid domain that surrounds the fluid domain. Several steady-state and time-transient model problems are presented as part of the code verification process. Nek5000/RS is designed for large-scale turbulence simulations, which will be the next step with this new MHD capability.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Phase evolution in two-phase alloys during severe plastic deformation

Herein, phase evolution in FCC metals with strongly interacting alloy components during severe plastic deformation is investigated using molecular dynamics simulations. Specifically, we study the alloy microstructure in steady state, nucleation and growth of precipitates in supersaturated alloys, and the decomposition of precipitates in undersaturated alloys. The results are compared to a modified effective temperature model, providing a physical understanding for the atomic processes underlying the model and a perspective on its strengths and weaknesses. Key observations in this work are nucleation and growth of precipitates during SPD at a temperature of 100 K; Gibbs-Thomson-like behavior relating solubility to precipitate size under steady-state shearing; a direct relationship between the effective temperature and the shear modulus; and the importance of cluster agglomeration during precipitate growth. The study also reveals that the mechanisms of forced chemical mixing depends on precipitate size, adding complications for effective temperature models describing inhomogeneous systems. The simulations are shown to provide good semiquantitative agreement with experimental findings reported in the literature.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Application of BISON to UO 2 MiniFuel fission gas release analysis

There has been a recent push to accelerate fuel qualification by developing revolutionary capabilities to reduce irradiation periods, and thereby, reduce the time required to qualify a new fuel system. One such capability is the MiniFuel irradiation capsule designed to miniaturize fuel samples and irradiate “mini” fuel samples under isothermal temperature conditions. MiniFuel allows steady-state irradiations to decouple the traditionally coupled fission rate (i.e., power) and temperature parameters to understand and generate microstructures observed in fuel operated in a commercial reactor. Furthermore, this process offers the possibility to gather in situ data as well as postirradiation or transient data such as thermal conductivity, specific heat, fission gas diffusion and release, etc. However, accelerating fuel qualification is not solely reliant on generating large amounts of data but also on developing an informed test matrix designed to rapidly generate impactful data. Additionally, this process is reliant on fuel performance codes, such as BISON, to evaluate MiniFuel irradiations using existing material models. This process pinpoints model/data gaps, identifies desired irradiation conditions, and subsequently supports model validation and development. This work describes the use of BISON to perform a number of sensitivity studies designed to understand conditions that lead to fission gas release (FGR) under steady-state isothermal irradiation conditions and temperature transient conditions. The model is applied to a UO 2 MiniFuel example and shows an overall good qualitative agreement with experimental FGR annealing tests under different temperature conditions. It also accounts well for microstructural effects on FGR. When quantitatively compared with FGR data from previously irradiated 103 MWd/kgU UO 2 discs under thermal annealing, the model shows a less satisfactory agreement with the experimental data. Finally, a UO 2 MiniFuel test matrix is proposed to help to extend the model's operational range and validate the new FGR model capabilities to higher burnups and transient conditions.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

Development of Steady-State and Dynamic Mass and Energy Constrained Neural Networks for Distributed Chemical Systems Using Noisy Transient Data

The paper presents the development of algorithms for mass and energy constrained neural network models that can exactly conserve the overall mass and energy of distributed chemical process systems, even though the noisy transient data used for optimal model training violate the same. In contrast to approximately satisfying mass and energy balance constraints of a system by soft penalization of objective function, algorithms have been developed for solving equality-constrained nonlinear optimization problems, thus providing the guarantee of exactly satisfying the system mass and energy conservation laws. For developing dynamic mass-energy constrained network models for distributed systems, hybrid series and parallel dynamic-static neural networks have been leveraged. The developed algorithms for solving both the training and forward problems are validated using both steady-state and dynamic data in the presence of various noise characteristics. The developed data-driven algorithms are flexible to exactly satisfy mass and energy balance constraints for dynamic chemical processes if the system holdup information is available. The proposed network structures and algorithms are applied to the development of data-driven lumped and distributed models of an adiabatic superheater/reheater system, a nonisothermal continuous stirred tank reactor, as well as an electrically heated plug-flow reactor system where one form of energy gets transformed to another. It has been observed that the mass-energy constrained neural networks yield a root mean squared error of <1% with respect to the system truth for the case studies evaluated in this work.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Statistically Steady State Large-Eddy Simulations Forced by an Idealized GCM: 1. Forcing Framework and Simulation Characteristics

Using large-eddy simulations (LES) systematically has the potential to inform parameterizations of subgrid-scale (SGS) processes in general circulation models (GCMs), such as turbulence, convection, and clouds. Here we show how LES can be run to emulate grid columns of GCMs to generate a library of LES across a cross-section of dynamical regimes. The LES setup replicates the thermodynamic and water budgets in GCM grid columns. Resolved horizontal and vertical transports of heat and water and large-scale pressure gradients from the GCM are prescribed as forcing in the LES. The LES satisfies the same (slab-ocean) surface boundary conditions as the GCM, leaving the LES temperatures free to adjust. Radiative transfer is treated in a unied but highly idealized manner (a semi-gray atmosphere without cloud radiative effects) in both the GCM and LES. We show that the LES with these forcing and boundary conditions reaches statistically steady states without nudging to reference profiles. These steady states provide a training dataset for developing GCM parameterizations. The same LES setup also provides a good basis for studying the cloud response to global warming.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Energy-efficient sorption-based gas clothes dryer systems

Standard electric resistance and fuel-driven dehydration technologies exhibit a maximum coefficient of performance of well below 1 mainly due to enthalpy losses associated with the air leaving the dehydration system. To improve energy efficiency, condensing dryer systems condense the moisture captured from a product in a closed-loop air circulation cycle. Existing condensing dehydration systems including heat pump dryers, however, need to significantly cool the air to achieve dehumidification. The added cooling and subsequent heating to return the air to a desired drying temperature consume substantial energy and thus reduce drying performance. Here, an innovative sorption-based gas dehydration system is proposed to overcome barriers deteriorating energy efficiency in existing gas, electric, or heat pump dryer systems. Decoupling latent and sensible loads, the system employs a liquid-desiccant solution to directly capture air humidity, thereby allowing circulation of the air in a closed loop to achieve high drying energy efficiency. In other words, the system captures waste latent heat from the moisture produced during the dehydration process and reuses it to improve energy efficiency. This study focuses on a comprehensive quasi-steady-state thermodynamic modeling of the proposed sorption-based dehydration concept employed for a gas clothes dryer application to predict transient response and overall drying performance (i.e., time and energy metrics). The analysis indicates the proposed sorption-based gas clothes dryer system can deliver a specific moisture extraction rate of 1.71 kg of water per kWh (i.e., a combined energy factor of 3.167 kg (6.98 lbm) of dry cloth per kWh) with a drying time of 44 min. This is a 112% energy improvement compared with state-of-the-art gas clothes dryers exhibiting a combined energy factor of 1.50 kg (3.3 lbm) of dry cloth per kWh. The technology pursued here can potentially be employed as a platform for many fuel-driven equipment to take advantage of available waste thermal energy in the environment instead of simply burning a fuel.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

An additively-manufactured molten salt-to-supercritical carbon di-oxide primary heat exchanger for solar thermal power generation – Design and techno-economic performance

The design and techno-economic performance of a compact additively manufactured (AM) molten salt (MS)-to-supercritical carbon di-oxide (sCO2) primary heat exchanger (PHE) for solar thermal application is described. The PHE design consists of sCO2 flow through an array of microscale pin fins while the MS flows through mm-scale rectangular channels. Constraints imposed by AM using laser powder bed fusion method are considered in the design. Structural and fluid flow simulations are performed to arrive at a viable design of the core and headers. A simplified one-dimensional steady state model for the PHE is developed including the impact of surface roughness from the AM process. A process-based cost model is used to determine the tradeoff between thermofluidic design and manufacturing cost. A parametric study is performed using the thermo-fluidic and cost models to determine the set of geometrical and flow variables that result in high power density and low cost, while restricting the pressure drop on the sCO2 side to less than 2% of line pressure. Flow rates of MS and sCO2 were varied over heat capacity rate ratios ranging from 0.2 to 1. Results indicate that it is possible to design a low-pressure drop AM PHE with an effectiveness of 90% and a power density in excess of 10 MW/m3 (including headers). Fabrication of representative nickel superalloy specimens are shown to demonstrate that low-porosity parts with the requisite dimensional tolerance of PHE core can be generated.

14 SOLAR ENERGY↗

DOE OSTI2025

Efficient up-conversion in CsPbBr 3 nanocrystals via phonon-driven exciton-polaron formation

Lead halide perovskite nanocrystals demonstrate efficient up-conversion, although the precise mechanism remains a subject of active research. This study utilizes steady-state and time-resolved spectroscopy methods to unravel the mechanism driving the up-conversion process in CsPbBr 3 nanocrystals. Employing above- and below-gap photoluminescence measurements, we extract a distinct phonon mode with an energy of ~7 meV and identify the Pb-Br-Pb bending mode as the phonon involved in the up-conversion process. This result was corroborated by Raman spectroscopy. We confirm an up-conversion efficiency reaching up to 75%. Transient absorption measurements under conditions of sub-gap excitation also unexpectedly reveal coherent phonons for the subset of nanocrystals undergoing up-conversion. This coherence implies that the up-conversion and subsequent relaxation is accompanied by a synchronized and phased lattice motion. This study reveals that efficient up-conversion in CsPbBr 3 nanocrystals is powered by a unique interplay between the soft lattice structure, phonons, and excited states dynamics.

Anti-Stokes Photoluminescence↗

DOE OSTI2020

Predicting Volume of Distribution in Humans: Performance of In Silico Methods for a Large Set of Structurally Diverse Clinical Compounds

Volume of distribution at steady state (V D,ss ) is one of the key pharmacokinetic parameters estimated during the drug discovery process. Despite considerable efforts to predict V D,ss , accuracy and choice of prediction methods remain a challenge, with evaluations constrained to a small set (<150) of compounds. To address these issues, a series of in silico methods for predicting human V D,ss directly from structure were evaluated using a large set of clinical compounds. Machine learning (ML) models were built to predict V D,ss directly and to predict input parameters required for mechanistic and empirical V D,ss predictions. In addition, log D, fraction unbound in plasma (fup), and blood-to-plasma partition ratio (BPR) were measured on 254 compounds to estimate the impact of measured data on predictive performance of mechanistic models. Furthermore, the impact of novel methodologies such as measuring partition (Kp) in adipocytes and myocytes (n = 189) on V D,ss predictions was also investigated. In predicting V D,ss directly from chemical structures, both mechanistic and empirical scaling using a combination of predicted rat and dog V D,ss demonstrated comparable performance (62%–71% within 3-fold). The direct ML model outperformed other in silico methods (75% within 3-fold, r 2 = 0.5, AAFE = 2.2) when built from a larger data set. Scaling to human from predicted V D,ss of either rat or dog yielded poor results (<47% within 3-fold). Measured fup and BPR improved performance of mechanistic V D,ss predictions significantly (81% within 3-fold, r 2 = 0.6, AAFE = 2.0). Adipocyte intracellular Kp showed good correlation to the V D,ss but was limited in estimating the compounds with low V D,ss .

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

A Risk-Informed Approach to Trustworthiness Assessment in Digital Twins-Based Autonomous Control

In autonomous control systems, digital twins (DTs) are used to perform diagnostic and prognostic functions. The trustworthiness of these DTs is dependent on quality and coverage of the training data, model accuracy and integrity of sensor data. This work introduces a methodology to determine the trustworthiness of a DT system given faulty sensor data using a risk informed approach. Bayesian Belief Networks (BBNs) are used to propagate uncertainties and determine the probability of trustable recommendations. The decision to trust the control action provided by the DT is based on the DT output, expert opinion, and severity of problems. The performance of DTs is reliant on the data they are trained on. When they encounter out of distribution data, the trustworthiness of the recommendations decreases. To address this issue, we include an expert component that provides input on sensor degradation. For this, we utilize a generative artificial intelligence (AI) model, such as Generative Pretrained Transformer (GPT). The GPT functions as an expert with broad knowledge. The GPT is fine-tuned to understand and discriminate sensor degradation scenarios using manufactured data. This methodology is demonstrated through a case study on a Nearly Autonomous Management and Control System (NAMAC) during a steady state scenario. Various sensor degradation types with different severity levels are considered. Degraded sensor data is processed by the DT system and the fine-tuned GPT. Finally, using the BBN, we combine the GPT information and the DT output with its sources of uncertainty. This provides an output regarding the trustworthiness of the DT recommendation.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Real-time evolution of texture and temperature during friction stir processing of a magnesium alloy: An operando neutron diffraction study

The real-time development of texture and the evolution of temperature during friction stir processing (FSP) of a Mg alloy were investigated using an operando neutron diffraction measurement. A novel approach was applied in this study where the real-time, quasi-steady state measurements performed as a function of position during FSP are converted to a Lagrangian dataset that reveals the transient behavior as a function of time. The in-situ FSP was carried out under two different thermo-mechanical processing conditions represented by the Zener-Hollomon parameter, Z. Further, the results show that: (i) a shear texture develops from the initial strong basal texture with the peak temperature reaching about 774 K during a low-Z processing and (ii) a strong off-normal texture develops with a peak temperature of 616 K during a high-Z processing. Moreover, time-temperature-texture diagrams were established to reveal the real-time development of the texture during the processing for both the low Z and high Z processing conditions. The changes in texture will be discussed in terms of plastic deformation mechanisms active during various stages of the FSP under the two different processing conditions.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Effect of Temperature on the Pilot-Scale Catalytic Pyrolysis of Loblolly Pine

A pilot-scale biomass catalytic pyrolysis unit with a nominal throughput of one tonne of biomass per day (1TPD) has been in operation since 2013 to investigate the process parameters that have the largest influence on biocrude yield, oxygen content, and chemical composition. A parametric study was conducted to investigate the effect of pyrolysis temperature, ranging from 433 to 581 °C, on biocrude yield and quality. A locally sourced loblolly pine feedstock and a commercially available, spray dried, nonzeolitic γ-Al 2 O 3 catalyst were used in individual experiments conducted at each pyrolysis temperature to achieve a minimum of 4 h of steady-state continuous operation. Typically, 600–800 kg of biomass was fed over a 12-h period, with one experiment extended to almost 29 h (1144-kg biomass fed) and one experiment interrupted by a process upset after just 7 h (411-kg biomass fed). Comprehensive analysis of collected gas, liquid, and solid products were used to calculate carbon balances (77% to 107%) for each experiment. The biocrude yield ranged from 12 to 18 wt % C and, in general, decreased with increasing pyrolysis temperature. The average steady-state biocrude yield as a function of temperature translated to a biocrude production rate between 40 and 50 gallons per dry ton. The biocrude oxygen content varied between 21 and 31 wt %, on a dry basis, and as expected, decreased with increasing pyrolysis temperature. The identified components in the semivolatile biocrude products are mostly methoxyphenols and other multiphenolic compounds. The multiphenolic compounds are demethoxylated at pyrolysis temperatures above 500 °C, producing biocrudes with higher concentrations of monophenols and polycyclic aromatic hydrocarbons. The concentration of anhydrosugars, like levoglucosan in the biocrudes, decreased with increasing pyrolysis temperature from ∼15 to ∼1 vol %.

biofuels↗