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DOE OSTI2025

LBNF Material Activation Study

This poster presents a study on material activation at the Fermilab Long-Baseline Neutrino Facility (LBNF), focusing on understanding how high-energy beams interact with surrounding materials to produce radioactive isotopes. Using a simplified model of the LBNF-20 bunker and the FLUKA Monte Carlo simulation tool, the project quantifies isotope production at key locations inside and outside the shielding structures. The study employs a two-step simulation process, tracking primary and secondary particles, and explores the impact of different geometric and material configurations on activation rates. Results include preliminary comparisons of simulated activation rates and suggest methods for refining geometry, improving accuracy, and efficiently simulating future configurations. This work informs the design and operation of the LBNF facility, aiding in radiation safety and shielding optimization. Future efforts will focus on sample iteration, automated simulations, and detailed residual isotope analyses to further enhance the understanding of material activation in high-energy physics environments.

Pietraszek, Jacob [Unlisted, US]↗

NASA NTRS2022

Simulations of Firn Processes Over the Greenland and Antarctic Ice Sheets: 1980-2021

Conversion of altimetry-derived ice-sheet volume change to mass requires an understanding of the evolution of the combined ice and air content within the firn column. In the absence of suitable techniques to observe the changes to the firn column across the entirety of an ice sheet, the firn column processes are typically modeled. Here, we present new simulations of firn processes over the Greenland and Antarctic ice sheets (GrIS and AIS) using the Community Firn Model and atmospheric reanalysis variables for more than four decades. A data set of more than 250 measured depth–density profiles from both ice sheets provides the basis of the calibration of the dry-snow densification scheme. The resulting scheme results in a reduction in the rate of densification, relative to a commonly used semi-empirical model, through a decreased dependence on the accumulation rate, a proxy for overburden stress. The 1980–2020 modeled firn column runoff, when combined with atmospheric variables from MERRA-2, generates realistic mean integrated surface mass balance values for the Greenland (+390 Gt yr(exp −1)) and Antarctic (+2612 Gt yr(exp −1)) ice sheets when compared to published model-ensemble means. We find that seasonal volume changes associated with firn air content are on average approximately 2.5 times larger than those associated with mass fluxes from surface processes for the AIS and 1.5 times larger for the GrIS; however, when averaged over multiple years, ice and air-volume fluctuations within the firn column are of comparable magnitudes. Between 1996 and 2019, the Greenland Ice Sheet lost nearly 5% of its firn air content, indicating a reduction in the total meltwater retention capability. Nearly all (94 %) of the meltwater produced over the Antarctic Ice Sheet is retained within the firn column through infiltration and refreezing.

Greenland↗

DOE OSTI2024

Using an Isotope Enabled Mass Balance to Evaluate Existing Land Surface Models

Abstract Land surface models (LSMs) play a crucial role in elucidating water and carbon cycles by simulating processes such as plant transpiration and evaporation from bare soil, yet calibration often relies on comparing LSM outputs of landscape total evapotranspiration ( ET ) and discharge with measured bulk fluxes. Discrepancies in partitioning into component fluxes predicted by various LSMs have been noted, prompting the need for improved evaluation methods. Stable water isotopes serve as effective tracers of component hydrologic fluxes, but data and model integration challenges have hindered their widespread application. Leveraging National Ecological Observation Network measurements of water isotope ratios at 16 US sites over 3 years combined with LSM‐modeled fluxes, we employed an isotope‐enabled mass balance framework to simulate ET isotope values ( δET ) within three operational LSMs (Mosaic, Noah, and VIC) to evaluate their partitioning. Models simulating δET values consistent with observations were deemed more reflective of water cycling in these ecosystems. Mosaic exhibited the best overall performance (Kling‐Gupta Efficiency of 0.28). For both Mosaic and Noah there were robust correlations between bare soil evaporation fraction and error (negative) as well as transpiration fraction and error (positive). We found the point at which errors are smallest ( x ‐intercept of the multi‐site regression) is at a higher transpiration fraction than is currently specified in the models. Which means that transpiration fraction is underestimated on average. Stable isotope tracers offer an additional tool for model evaluation and identifying areas for improvement, potentially enhancing LSM simulations and our understanding of land‐surface hydrologic processes.

58 GEOSCIENCES↗

DOE OSTI2023

An Ensemble of Neural Networks for Moist Physics Processes, Its Generalizability and Stable Integration

Abstract With the recent advances in data science, machine learning has been increasingly applied to convection and cloud parameterizations in global climate models (GCMs). This study extends the work of Han et al. (2020, https://doi.org/10.1029/2020MS002076 ) and uses an ensemble of 32‐layer deep convolutional residual neural networks, referred to as ResCu‐en, to emulate convection and cloud processes simulated by a superparameterized GCM, SPCAM. ResCu‐en predicts GCM grid‐scale temperature and moisture tendencies, and cloud liquid and ice water contents from moist physics processes. The surface rainfall is derived from the column‐integrated moisture tendency. The prediction uncertainty inherent in deep learning algorithms in emulating the moist physics is reduced by ensemble averaging. Results in 1‐year independent offline validation show that ResCu‐en has high prediction accuracy for all output variables, both in the current climate and in a warmer climate with +4K sea surface temperature. The analysis of different neural net configurations shows that the success to generalize in a warmer climate is attributed to convective memory and the 1‐dimensional convolution layers incorporated into ResCu‐en. We further implement a member of ResCu‐en into CAM5 with real world geography and run the neural‐network‐enabled CAM5 (NCAM) for 5 years without encountering any numerical integration instability. The simulation generally captures the global distribution of the mean precipitation, with a better simulation of precipitation intensity and diurnal cycle. However, there are large biases in temperature and moisture in high latitudes. These results highlight the importance of convective memory and demonstrate the potential for machine learning to enhance climate modeling.

Meteorology & Atmospheric Sciences↗

DOE OSTI2023

Comparing Measured Driver Behavior Distributions to Results from Car-Following Models using SUMO and Real-World Vehicle Trajectories from Radar: SUMO Default vs. Radar-Measured CF model Parameters

In this study, the physical principles governing car-following (CF) behavior and their impact on traffic flow at signalized intersections are investigated. High temporal-resolution radar data is used to provide valuable insights into actual CF behavior, including acceleration, deceleration, and time headway distribution. Demand-calibrated SUMO simulations are run using empirical CF parameter distributions, and three CF models are evaluated: IDM, EIDM, and Krauss. By emulating radar data in SUMO and processing simulated vehicle traces, discrepancies between empirical and simulated parameter distributions are identified. Further analysis includes comparisons with default SUMO CF model parameters. The findings reveal that measured accelerations differ from CF model parameter accelerations and using the empirical value ($\mu = 0.89m/s^2$) leads to unrealistic simulations that fail volume-based calibration. Default parameters for all three models reasonably approximate the mean and median of measured parameters, but fail to capture the true distribution shape, partly due to homogeneity when using default parameters. The results show that it is more effective to simulate with the default parameters provided by SUMO rather than using measurements of real-world distributions without additional calibration. Future work will investigate closing the loop between the measured real-world and SUMO distributions using traditional calibration tactics, as well as assess the impact of calibrated vs. default CF parameters on simulation outputs like fuel consumption.

Schrader, Max (ORCID:0000000336737672)↗

NASA NTRS1994

Simulation studies for surfaces and materials strength

Computer simulation studies were conducted to investigate materials properties. All the calculations were carried out using atomic level simulation techniques which are based on semiempirical or model functions. During this project, functions with varying degrees of complexity were derived and employed in simulations. Investigations include covalently bonded materials as well as systems involving metal atoms. For small clusters calculated results provided information on various energy- and structure-related properties along with vibrational characteristics. Also, energy barriers for configurational transitions were calculated for selected cases. Simulation calculations for surfaces produced new results in areas related to surface energies, reconstructions and relaxations, surface defects, surface stresses as well as adsorption and nucleation processes. Simulations were also performed on bulk materials. Calculated results provided an atomic level understanding on energetics and structures of point defects, crystal stability, elastic properties, and materials strength for various systems. Calculations involving polymeric materials include studies of polar polymer melts and polymer/solid interfaces. Simulations employing specially developed codes provided significant information about energetics and conformational characteristics of different polymeric chains.

Halicioglu, Timur↗

NASA NTRS2007

Development of Probabilistic Structural Analysis Integrated with Manufacturing Processes

An effort has been initiated to integrate manufacturing process simulations with probabilistic structural analyses in order to capture the important impacts of manufacturing uncertainties on component stress levels and life. Two physics-based manufacturing process models (one for powdered metal forging and the other for annular deformation resistance welding) have been linked to the NESSUS structural analysis code. This paper describes the methodology developed to perform this integration including several examples. Although this effort is still underway, particularly for full integration of a probabilistic analysis, the progress to date has been encouraging and a software interface that implements the methodology has been developed. The purpose of this paper is to report this preliminary development.

Pai, Shantaram S.↗

NASA NTRS1981

An approach to real-time simulation using parallel processing

Current applications of real-time simulations to the development of complex aircraft propulsion system controls have demonstrated the need for accurate, portable, and low-cost simulators. This paper presents a preliminary simulator design that uses a parallel computer organization to provide these features. The hardware and software for this prototype simulator are discussed. A detailed discussion of the inter-computer data transfer mechanism is also presented.

Blech, R. A.↗

NASA NTRS1994

Life support systems analysis and technical trades for a lunar outpost

The NASA/JPL life support systems analysis (LISSA) software tool was used to perform life support system analysis and technology trades for a Lunar Outpost. The life support system was modeled using a chemical process simulation program on a steady-state, one-person, daily basis. Inputs to the LiSSA model include metabolic balance load data, hygiene load data, technology selection, process operational assumptions and mission parameter assumptions. A baseline set of technologies has been used against which comparisons have been made by running twenty-two cases with technology substitutions. System, subsystem, and technology weights and powers are compared for a crew of 4 and missions of 90 and 600 days. By assigning a weight value to power, equivalent system weights are compared. Several less-developed technologies show potential advantages over the baseline. Solid waste treatment technologies show weight and power disadvantages but one could have benefits associated with the reduction of hazardous wastes and very long missions. Technology development towards reducing the weight of resupplies and lighter materials of construction was recommended. It was also recommended that as technologies are funded for development, contractors should be required to generate and report data useful for quantitative technology comparisons.

Ferrall, J. F.↗

DOE OSTI2026

Assessing the Accuracy of Property Model Predictions for Cost Optimization of Desalination Technologies

Accurate modeling of seawater thermophysical and thermodynamic properties is critical for optimizing desalination processes. This study compares three seawater property models, a Reaktoro multicomponent model, the thermophysical seawater properties library from the Massachusetts Institute of Technology, and a simplified sodium chloride model, in the context of levelized cost of water (LCOW) minimization for reverse osmosis (RO) and mechanical vapor compression systems. Process simulations and cost optimizations reveal that although all three models yield comparable LCOW and specific energy consumption (SEC) estimates under baseline conditions, deviations among their predictions increase with salinity. Relative differences in LCOW and SEC reach up to 6% and 8%, respectively. RO results show greater variability due to differences in osmotic pressure predictions, which affect pressure constraints at high recoveries. Computational performance varies substantially; specifically, Reaktoro simulations are up to 28 times slower than empirical models due to their detailed equilibrium calculations. These results suggest that empirical models offer acceptable accuracy for routine desalination process design, while Reaktoro provides advantages in scenarios requiring detailed speciation, such as scaling or pH adjustment studies. These findings underscore the importance of selecting appropriate property models based on the modeling objective of desalination applications and motivate future work integrating thermodynamic rigor with empirical efficiency.

Physical and chemical properties↗

DOE OSTI2020

Simulation of DWPF Chemical Process Cell Using a Reaction Calorimeter

The Defense Waste Processing Facility (DWPF) converts liquid radioactive waste into glass using a Sludge Receipt Adjustment Tank (SRAT) and Slurry Mix Evaporator (SME) in addition to other processes. The SRAT and SME processes can be simulated on a small-scale using the Mettler-Toledo Reaction Calorimeter (RC1). This equipment is able to automate the execution and monitoring of experiments involving nonradioactive chemical simulants. During the chemical processing of sludge in the SRAT process, foam begins to form and can foam over the reaction vessel. The current antifoam used at DWPF creates a risk of increased flammability and has a limited pH range. Usually, mercury is circulated back into the reaction vessel during the SRAT process, making it difficult to remove. To combat foam over, novel antifoams will be tested during the boil-up process using the RC1. To improve mercury extraction, 3D-printed coalescer columns will be tested within the Mercury Water Wash Tank (MWWT) of the RC1. Testing of Simulant Sludge Batch 6 (SB6) was performed to ensure: the sludge would form a stable foam at boiling during full SRAT/SME runs; the antifoam would reduce the amount of foam within the vessel The sludge and a Precipitate Reactor Feed Tank (PRFT) simulant with monosodium titanate (MST) were mixed separately, combined, and then heated to boiling. As shown below, the antifoam used was able to eliminate the foam within the vessel, as expected. The FormLabs Form2 3D Printer can be used to create roughly 3-inch tall and 1-inch diameter columns from clear and ceramic resins. The columns will utilize chemical and physical forces to draw the mercury out of a condensed offgas stream leaving the RC1 reaction vessel. These columns will be tested during full SRAT/SME runs on the basis of how well they are able to collect elemental mercury. In preparation for full-scale runs, the RC1 equipment was assembled onto a custom table and tested with water runs. The software used to control automation, iControl, has a user-friendly interface that allows the experiment to be altered as needed. An example of the data recorded during an experimental run is represented in Figure 1 below. Water runs were utilized as a way to prove if the RC1 system was functioning in the following areas: Boil-up rates, Chemical additions using pumps, Temperature readings, Balance readings, Probe readings, Offgas system, Agitation. Future Plans: Five novel antifoams will be tested during full SRAT/SME runs. The most effective antifoam will be utilized in DWPF's Chemical Process Cell. The design of the Mercury Extraction Coalescer can be further manipulated to increase the extraction of not only elemental mercury, but other heavy metals in environmental remediation processes as well.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2022

Predictive modeling of a subcritical pulverized-coal power plant for optimization: Parameter estimation, validation, and application

As renewable power generation deployment increases, fossil fuel plants are increasingly required to operate more flexibly. Many coal-fired power plants were originally designed to operate at base load and do not operate optimally at partial load. Predictive first-principles plant-wide models can be employed to identify opportunities for flexibility improvements and diagnose low-load operating issues. This paper describes the application of the Institute for the Design of Advanced Energy Systems Integrated Platform (IDAES) to model and optimize flexible power plant operations. The key benefits of using IDAES are that it provides an open-source, fully equation-oriented modeling framework for efficient modular model construction, reuse, and customization, together with a mathematical optimization framework leveraging powerful, state-of-the-art solvers. The process systems engineering workflow from predictive process simulation to parameter estimation, model validation, and plant optimization is applicable to a variety of existing and next-generation energy systems as well as other chemical and environmental processes. Here, to demonstrate this capability, a physics-based, steady-state model was developed to improve full- and part-load performance of the Escalante Generating Station, a 245 MWe (net) subcritical pulverized coal-fired power plant owned and operated by Tri-State Generation and Transmission Association. Specifically, sixty-nine model parameters were simultaneously estimated from several months of operating data enabling prediction of flow rates, temperatures, pressures, and steam quality throughout the plant. The validated model was leveraged by Escalante to reduce the minimum operating load from 90 MW to 50 MW by diagnosing a low-load water-hammer issue, enabling coal usage and emissions reductions during periods of low power demand. Additionally, opportunities for heat rate reduction (i.e., efficiency improvement) through a steeper sliding-pressure approach to load-following and optimization of other boiler operating variables were also identified and quantified. For example, a potential efficiency improvement of 0.7 percentage points was observed at half-load operation.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2024

Experimental analysis of convective drying of paper and board

Conventional multi-cylinder drying of paper and board involves a mixture of conductive drying from steam-heated dryer cylinders and convective drying by the flow of heated air over the surface of the paper web in the pockets. Pocket ventilation is a critical component in assisting heat and mass transfer during the drying process but is the primary contributor towards removing evaporated water from the web. Air temperature, velocity, and humidity are critical parameters involved in the convective drying process. This paper covers an experimental study involving the design and development of a small lab-scale setup for convective drying of various grades of paper and board, monitoring multiple parameters like paper temperature, moisture content, air humidity, temperature, and velocity measured in situ as the drying proceeds with continuous and accurate sampling capabilities for all parameters in the sample and the system. Instantaneous drying rates, heat, and mass transfer coefficients were also deduced for every time step till the paper completely dried. Furthermore, the coefficients obtained were also reported in the form of dimensionless correlations, and the results were compared against traditional correlations used in the modelling of paper drying. Furthermore, this data will be useful in process development, modelling, design, and the paper drying process simulation.

42 ENGINEERING↗

DOE OSTI2023

M-Star ® Software Test and Verification

Savannah River Mission Completion (SRMC) currently manages the risk for retained hydrogen in the Defense Waste Processing Facility (DWPF) vessels by implementing a Retained Hydrogen Program. The current program relies on Sludge Batch (SB) 8 Gas Chromatograph data and on conservative assumptions concerning gas release and retention to determine allowable vessel Quiescent time (Q-time). The authors identified M-Star ® CFD as a software that could simulate processes such as fluid flow, heat transfer, species transport, chemical reactions, particle transport, and retained hydrogen gas release. Preliminary simulation results suggest that more realistic assumptions on gas retention and release may be feasible for the DWPF retained hydrogen program. Because of the desire to use the M-Star ® software to perform analyses that support nuclear safety, SRMC has requested Savannah River National Laboratory (SRNL) to upgrade the software classification level of M-Star ® CFD from level D to level A to perform analyses that support nuclear safety.

08 HYDROGEN↗

DOE OSTI2022

Optimization Modeling for Advanced Syngas to Olefin Reactive Systems

Reactor designs with mixed catalysts play an important role in transforming a multiple reactor system to single-shot reactors. In addition to savings in capital and ease of implementation, single-shot reactors are useful to break equilibrium limitations, thereby increasing the yield and selectivity of desired product as shown in previous studies. However, the nonlinear and highly exothermic nature of mixed-catalyst systems makes it difficult for commercial process simulation and optimization tools to optimize these systems. This study describes the development and application of optimization strategies for mixed-catalyst, single-shot reactors for syngas to olefin (STO) processes. Finding the optimal catalyst distribution is challenging and requires advanced solution strategies for singular optimal control problems, which are poorly conditioned and often lead to flat response surfaces. The graded bed and partial-moving finite-element approaches are used to find the optimal catalyst distribution that maximizes the olefins yield. A 1.3% increase in the yield is observed from one zone to three zones. The yield further improves from three zones to the exact infinite dimensional solution by 0.2%. This improvement can be realized in practice by changing only the catalyst distribution, without any extra investment. Lastly, the results suggest that a mixed-catalyst single shot reactor bed can be applied to other reaction mechanisms to increase reactor performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2008

Extraction of Thermal Performance Values from Samples in the Lunar Dust Adhesion Bell Jar

A simulation chamber has been developed to test the performance of thermal control surfaces under dusty lunar conditions. The lunar dust adhesion bell jar (LDAB) is a diffusion pumped vacuum chamber (10(exp -8) Torr) built to test material samples less than about 7 cm in diameter. The LDAB has the following lunar dust simulant processing capabilities: heating and cooling while stirring in order to degas and remove adsorbed water; RF air-plasma for activating the dust and for organic contaminant removal; RF H/He-plasma to simulate solar wind; dust sieving system for controlling particle sizes; and a controlled means of introducing the activated dust to the samples under study. The LDAB is also fitted with an in situ Xe arc lamp solar simulator, and a cold box that can reach 30 K. Samples of thermal control surfaces (2.5 cm diameter) are introduced into the chamber for calorimetric evaluation using thermocouple instrumentation. The object of this paper is to present a thermal model of the samples under test conditions and to outline the procedure to extract the absorptance, emittance, and thermal efficiency from the pristine and sub-monolayer dust covered samples.

Gaier, James R.↗

NASA NTRS2010

Extraction of Thermal Performance Values from Samples in the Lunar Dust Adhesion Bell Jar

Gaier, James R.↗

NASA NTRS1990

A comparison of real-time blade-element and rotor-map helicopter simulations using parallel processing

In recent efforts by NASA, the Army, and Advanced Rotorcraft Technology, Inc. (ART), the application of parallel processing techniques to real-time simulation have been studied. Traditionally, real-time helicopter simulations have omitted the modeling of high-frequency phenomena in order to achieve real-time operation on affordable computers. Parallel processing technology can now provide the means for significantly improving the fidelity of real-time simulation, and one specific area for improvement is the modeling of rotor dynamics. This paper focuses on the results of a piloted simulation in which a traditional rotor-map mathematical model was compared with a more sophisticated blade-element mathematical model that had been implemented using parallel processing hardware and software technology.

Corliss, Lloyd↗