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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Special Issue: Microbial Community Modeling: Prediction of Microbial Interactions and Community Dynamics

Microbial communities are networks of species, the interaction structure of which dynamically reorganizes in a varying environment. Even in a static condition, community dynamics are often difficult to predict due to highly nonlinear interspecies interactions. Understanding fundamental principles of microbial interactions is therefore key for predicting and harnessing community function and properties. As extensively reviewed previously, mathematical models and computational methods that can predictively link interactions to community behaviors are indispensable tools for achieving this goal. This Special Issue of Processes collects eleven papers from lead scientists and researchers active in the area under the topic of “Microbial Community Modeling: Prediction of Microbial Interactions and Community Dynamics.” The collected papers cover various topics of interest: 1) two review/opinion papers discussing recent advances in biofilm modeling and specific issues for successful collaboration between experimentalists and theorists, 2) one paper on the dynamics of complex environmental communities, 3) six papers dealing with fundamental aspects of microbial interactions and stability in model communities, and 4) two papers on the development and utilization of microbial consortia for biotechnology applications.

Song, Hyun-Seob (ORCID:0000000221546358)↗
DOE OSTI2014

A predictive model of shell morphology in CdSe/CdS core/shell quantum dots

Lattice mismatch in core/shell nanoparticles occurs when the core and shell materials have different lattice parameters. When there is a significant lattice mismatch, a coherent core-shell interface results in substantial lattice strain energy, which can affect the shell morphology. The shell can be of uniform thickness or can be rough, having thin and thick regions. A smooth shell minimizes the surface energy at the expense of increased lattice strain energy and a rough shell does the opposite. A quantitative treatment of the lattice strain energy in determining the shell morphology of CdSe/CdS core/shell nanoparticles is presented here. We use the inhomogeneity in hole tunneling rates through the shell to adsorbed hole acceptors to quantify the extent of shell thickness inhomogeneity. The results can be understood in terms of a model based on elastic continuum calculations, which indicate that the lattice strain energy depends on both core size and shell thickness. The model assumes thermodynamic equilibrium, i.e., that the shell morphology corresponds to a minimum total (lattice strain plus surface) energy. Comparison with the experimental results indicates that CdSe/CdS nanoparticles undergo an abrupt transition from smooth to rough shells when the total lattice strain energy exceeds about 27 eV or the strain energy density exceeds 0.59 eV/nm{sup 2}. We also find that the predictions of this model are not followed for CdSe/CdS nanoparticles when the shell is deposited at very low temperature and therefore equilibrium is not established.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Ecosystem Controls on Methylmercury Production by Periphyton Biofilms in a Contaminated Stream: Implications for Predictive Modeling

Periphyton biofilms produce a substantial fraction of the overall monomethylmercury (MMHg) flux in East Fork Poplar Creek, an industrially contaminated, freshwater creek in Oak Ridge, Tennessee. We examined periphyton MMHg production across seasons, locations, and light conditions using mercury stable isotopes. Methylation and demethylation rate potentials (km, trans av and kd, trans av, respectively) were calculated using a transient availability kinetic model. Light exposure and season were significant predictors of km, trans av, with greater values in full light exposure and in the summer. Season, light exposure, and location were significant predictors of kd, trans av, which was highest in dark conditions, in the spring, and at the upstream location. Light exposure was the controlling factor for net MMHg production, with positive production for periphyton grown under full light exposure and net demethylation for periphyton grown in the dark. Ambient MMHg and km, trans av were significantly correlated. Transient availability rate potentials were 15 times higher for km and 9 times higher for kd compared to full availability rate potentials (km, full av and kd, full av) calculated at 1 d. No significant model for the prediction of km, full av or kd, full av could be constructed using light, season, and location. In addition, there were no significant differences among treatments for the full availability km, full av, kd, full av, or net MMHg calculated using the full availability rate potentials. km, full av was not correlated with ambient MMHg concentrations. The present results underscore the importance of applying transient availability kinetics to MMHg production data when estimating MMHg production potential and flux.

Schwartz, Grace E.↗
DOE OSTI2017

Predictive modeling to de-risk bio-based manufacturing by adapting to variability in lignocellulosic biomass supply

Commercial-scale bio-refineries are designed to process 2000 tons/day of single lignocellulosic biomass. Several geographical areas in the United States generate diverse feedstocks that, when combined, can be substantial for bio-based manufacturing. Blending multiple feedstocks is a strategy being investigated to expand bio-based manufacturing outside Corn Belt. Here in this study, we developed a model to predict continuous envelopes of biomass blends that are optimal for a given pretreatment condition to achieve a predetermined sugar yield or vice versa. For example, our model predicted more than 60% glucose yield can be achieved by treating an equal part blend of energy cane, corn stover, and switchgrass with alkali pretreatment at 120 °C for 14.8 h. Finally, by using ionic liquid to pretreat an equal part blend of the biomass feedstocks at 160 °C for 2.2 h, we achieved 87.6% glucose yield. Such a predictive model can potentially overcome dependence on a single feedstock.

09 BIOMASS FUELS↗
DOE OSTI2023

Milestone 1.2.13: Preliminary Measurements of Radiolytic Nitric Acid Formation to Support Predictive Model Validation

Predictive computational models have been developed to support the technical basis for extended dry storage of aluminum-clad spent nuclear fuel (ASNF) in helium-backfilled cannisters. To date, these models have been optimized on a variety of irradiation experiments designed to elucidate the radiation-induced formation of molecular hydrogen gas, a radiolysis product that is potentially problematic for the safe storage of ASNF. However, the yield of nitric acid (HNO3) has not been determined, despite conservative predictions of its formation (300–4000 ppm) in 1% residual air environments irradiated in contact with ASNF. HNO3, another problematic radiolysis product, can lead to enhanced corrosion and potentially compromise storage canister integrity. Thus, to support the validation of predictive computer models, we report the measurement and quantification of HNO3 from the gamma irradiation of aluminum alloy coupons in humid air.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2022

Can reanalysis products outperform mesoscale numerical weather prediction models in modeling the wind resource in simple terrain?

Mesoscale numerical weather prediction (NWP) models are generally considered more accurate than reanalysis products in characterizing the wind resource at heights of interest for wind energy, given their finer spatial resolution and more comprehensive physics. However, advancements in the latest ERA-5 reanalysis product motivate an assessment on whether ERA-5 can model wind speeds as well as a state-of-the-art NWP model – the Weather Research and Forecasting (WRF) Model. We consider this research question for both simple terrain and offshore applications. Specifically, we compare wind profiles from ERA-5 and the preliminary WRF runs of the Wind Integration National Dataset (WIND) Toolkit Long-term Ensemble Dataset (WTK-LED) to those observed by lidars at a site in Oklahoma, United States, and in a United States Atlantic offshore wind energy area. We find that ERA-5 shows a significant negative bias (~-1ms-1) at both locations, with a larger bias at the land-based site. WTK-LED-predicted wind speed profiles show a limited negative bias (~-0.5ms-1) offshore and a slight positive bias (~+0.5ms-1) at the land-based site. On the other hand, we find that ERA-5 outperforms WTK-LED in terms of the centered root-mean-square error (cRMSE) and correlation coefficient, for both the land-based and offshore cases, in all atmospheric stability conditions. We find that WTK-LED's higher cRMSE is caused by its tendency to overpredict the amplitude of the wind speed diurnal cycle. At the land-based site, this is partially caused by wind plant wake effects not being accurately captured by WTK-LED.

17 WIND ENERGY↗
NASA NTRS2021

Self-Consistent Implementation of a Zero-Equation Transport Model Into a Predictive Model for a Hall Effect Thruster

The performance of an axisymmetric multi-fluid Hall thruster code that incorporates a self-consistent, data-driven closure model for the anomalous electron transport is investigated. Five different operating conditions of the H9 magnetically shielded Hall thruster are simulated with the Jet Propulsion Laboratory’s Hall2De. In order to capture the inherent uncertainty associated with the closure model, O(100) simulations are run for each condition, each of using a coefficient set sampled randomly from a probability distribution. The results of these simulations provide probabilistic predictions of thruster performance quantities including thrust, and discharge current, as well as several component efficiencies and centerline plasma properties. The model is found to yield converged solutions at all conditions, with large 10 kHzrange oscillations and performance trends with voltage and flow rate similar to experiment. The model under-predicts the thrust by 15-25% and over-predicts the discharge current by 20% on average compared to experiments at the same discharge voltage and mass flow rate. This performance discrepancy is due to lower beam utilization, mass utilization, and divergence efficiency than experiment, resulting from high Hall parameters in the acceleration region, which lead to a protracted ion acceleration region. The physical processes underlying this result are discussed in the context of future data-driven modeling efforts.

Jorns, Benjamin A.↗
DOE OSTI2021

A Multirange Vehicle Speed Prediction With Application to Model Predictive Control-Based Integrated Power and Thermal Management of Connected Hybrid Electric Vehicles

Abstract Connectivity and automated driving technologies have opened up new research directions in the energy management of vehicles which exploit look-ahead preview and enhance the situational awareness. Despite this advancement, the vehicle speed preview that can be obtained from vehicle-to-vehicle/infrastructure (V2V/I) communications is often limited to a relatively short time-horizon. The vehicular energy systems, specifically those of the electrified vehicles, consist of multiple interacting power and thermal subsystems that respond over different time-scales. Consequently, their optimal energy management can greatly benefit from long-term speed prediction beyond that available through V2V/I communications. Accurately extending the look-ahead preview, on the other hand, is fundamentally challenging due to the dynamic nature of the traffic environment. To address this challenge, we propose a data-driven multirange vehicle speed prediction strategy for arterial corridors with signalized intersections, providing the vehicle speed preview for three different ranges, i.e., short-, medium-, and long-range. The short-range preview is obtained by V2V/I communications. The medium-range preview is realized using a neural network (NN), while the long-range preview is predicted based on a Bayesian network (BN). The predictions are updated in real-time based on the current state of traffic and incorporated into a multihorizon model predictive control (MH-MPC) for integrated power and thermal management (iPTM) of connected vehicles. The results of design and evaluation of the performance of the proposed data-informed MH-MPC for iPTM of connected hybrid electric vehicles (HEVs) using traffic data for real-world city driving are reported.

Automation & Control Systems↗
DOE OSTI2024

Characterization of Precipitation-Strengthening Heat-Resistant Austenitic Stainless Steels for Life-Prediction Modeling

In this study, the role of minor alloying additions in 347H stainless steels (UNS34709, ASTM A240/240M) on creep-rupture properties at 650-750°C and microstructure evolution during isothermal exposure at 750°C has been investigated, aiming to provide the experimental dataset as boundary conditions of physics-based modeling for material/component life prediction. Four different 347H heats containing various amounts of boron and nitrogen additions were prepared and evaluated. The combined additions of B and N are found to stabilize the strengthening secondary M23C6 carbides and retarding the transition from M23C6 to sigma phase precipitates during thermal exposure. The observed kinetics of microstructure evolution reasonably explains the improvement of creep-rupture properties of 347H stainless steels with the B and N additions.

Yamamoto, Yukinori↗
DOE OSTI2022

Analysis of predicted mean vote-based model predictive control for residential HVAC systems

Model Predictive Control (MPC) is an advanced process control method that has attracted much attention in building heating, ventilation, and air conditioning (HVAC) systems. Here, this paper analyzes the optimal precooling performance in residential buildings using MPC with two different comfort indices, namely, temperature and predicted mean vote (PMV). It first formulates, for each comfort index, an optimization problem that accounts for different factors, such as weather, home thermal condition, prediction horizon, time-of use (TOU) utility rate, and rated cooling capacity. The problem is then solved, resulting in an MPC strategy that determines the HVAC on/off control signal and minimizes energy cost over a receding time horizon while maintaining thermal comfort. The energy performance difference between temperature-based and PMV-based MPC strategies is subsequently investigated, especially in light of the interior wall surface temperature and under different combinations of the factors. Extensive simulation results demonstrated that the proposed MPC strategies are adaptive and their performances depend primarily on weather, home thermal condition, and prediction horizon, while the impact of TOU utility rate and rated cooling capacity is relatively small. Because the PMV-based MPC strategy can take advantage of the lower interior wall surface temperature due to precooling, it resulted in 8–45% cost savings for the scenarios investigated and an average increase of 0.042–0.113 in the absolute value of the PMV index compared to the temperature-based MPC strategy.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

OVERVIEW OF ALUMINUM CLADDING OXIDE PREDICTION MODELS FOR EUROPEAN HIGH POWER RESEARCH REACTORS

UMo/Al dispersion fuel clad with aluminum alloy is a primary candidate fuel form that is being developed for the European high power research reactors (EUHPRR). In BR2 tests, water oxidation of cladding grew thick due to high powers. Excessive cladding oxidation is a cause for serious fuel performance degradation in a test while fuel performance itself may be sound. It is necessary for a fuel designer to have a reliable model to predict cladding oxidation for fuel licensing activities. Using oxide data from BR2 tests, this study reviews the prominent oxide prediction models and cladding-to-coolant heat transfer correlations. Because the oxide growth models were based upon different coolant flow conditions from those of the EUHPRR, the effect of the heat transfer correlations at the cladding-to-coolant interface on the oxide thickness prediction were also examined. The best combination of the oxide prediction model and the heat transfer correlation applicable for the EUHPRR is recommended. Since the existing oxide models were all developed for aluminum alloy types different from those currently considered for the EUHPRR, the effect of cladding alloy type is also discussed.

Research reactor fuel, aluminum oxidation, in-pile↗
NASA NTRS1982

Life prediction model comparisons

Predictive failure models for nickel cadmium batteries are discussed. Emphasis is given to the McDermott model, the Lander model, and the JPL failure model.

Schulmam, I. M.↗
DOE OSTI2014

Reduced model prediction of electron temperature profiles in microtearing-dominated National Spherical Torus eXperiment plasmas

A representative H-mode discharge from the National Spherical Torus eXperiment is studied in detail to utilize it as a basis for a time-evolving prediction of the electron temperature profile using an appropriate reduced transport model. The time evolution of characteristic plasma variables such as β{sub e}, ν{sub e}{sup ∗}, the MHD α parameter, and the gradient scale lengths of T{sub e}, T{sub i}, and n{sub e} were examined as a prelude to performing linear gyrokinetic calculations to determine the fastest growing micro instability at various times and locations throughout the discharge. The inferences from the parameter evolutions and the linear stability calculations were consistent. Early in the discharge, when β{sub e} and ν{sub e}{sup ∗} were relatively low, ballooning parity modes were dominant. As time progressed and both β{sub e} and ν{sub e}{sup ∗} increased, microtearing became the dominant low-k{sub θ} mode, especially in the outer half of the plasma. There are instances in time and radius, however, where other modes, at higher-k{sub θ}, may, in addition to microtearing, be important for driving electron transport. Given these results, the Rebut-Lallia-Watkins (RLW) electron thermal diffusivity model, which is based on microtearing-induced transport, was used to predict the time-evolving electron temperature across most of the profile. The results indicate that RLW does a good job of predicting T{sub e} for times and locations where microtearing was determined to be important, but not as well when microtearing was predicted to be stable or subdominant.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗