Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “predictive”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Prediction of the Thermal Runaway Limit and Optimal Operation of Heat Transfer-Limited, Fixed-Bed Reactor Systems

Here, we derive a new prediction for thermal runaway starting from the alpha model for fixed-bed reactor systems. This method accounts for thermal resistance internal to the reactor tube and the radial temperature gradients that result. To showcase our method, we compare its predictions to other common criteria for thermal runaway using o-xylene oxidation as the example chemistry. Even in systems where internal heat transfer is negligible, the empirical practical design criterion for thermal runaway is inaccurate. For cases where internal heat transfer is relevant, our runaway limit is more stringent than limits derived from simpler 1-D models. To augment our work, we optimize the product yield with the thermal runaway constraint using orthogonal collocation. Using the alpha model, the results illustrate that the thermal runaway limit can be accurately determined using either numerical or analytical methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Prediction of intrinsic topological superconductivity in Mn-doped GeTe monolayer from first-principles

The recent discovery of topological superconductors (TSCs) has sparked enormous interest. The realization of TSC requires a delicate tuning of multiple microscopic parameters, which remains a great challenge. Here, we develop a first-principles approach to quantify realistic conditions of TSC by solving self-consistently Bogoliubov-de Gennes equation based on a Wannier function construction of band structure, in presence of Rashba spin-orbit coupling, Zeeman splitting and electron-phonon coupling. We further demonstrate the power of this method by predicting the Mn-doped GeTe (Ge 1- x Mn x Te) monolayer—a well-known dilute magnetic semiconductor showing superconductivity under hole doping—to be a Class D TSC with Chern number of -1 and chiral Majorana edge modes. By constructing a first-principles phase diagram in the parameter space of temperature and Mn concentration, we propose the TSC phase can be induced at a lower-limit transition temperature of ~40 mK and the Mn concentration of x ~0.015%. Our approach can be generally applied to TSCs with a phonon-mediated pairing, providing useful guidance for future experiments.

Chemistry↗
DOE OSTI2023

Improving nuclear data evaluations with predictive reaction theory and indirect measurements

Nuclear reaction data required for astrophysics and applications is incomplete, as not all nuclear reactions can be measured or reliably predicted. Neutron-induced reactions involving unstable targets are particularly challenging, but often critical for simulations. In response to this need, indirect approaches, such as the surrogate reaction method, have been developed. Nuclear theory is key to extract reliable cross sections from such indirect measurements. We describe ongoing efforts to expand the theoretical capabilities that enable surrogate reaction measurements. We focus on microscopic predictions for charged-particle inelastic scattering, uncertainty-quantified optical nucleon-nucleus models, and neural-network enhanced parameter inference.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

The Area Localized Coupled Model for Analytical Mean Flow Prediction in Arbitrary Wind Farm Geometries

This work introduces the area localized coupled (ALC) model, which extends the applicability of approaches that couple classical wake superposition models and atmospheric boundary layer models to wind farms with arbitrary layouts. Coupling wake and top–down boundary layer models is particularly challenging since the latter requires averaging over planform areas associated with turbine-specific regions of the flow that need to be specified. The ALC model uses Voronoi tessellation to define this local area around each turbine. A top–down description of a developing internal boundary layer is then applied over Voronoi cells upstream of each turbine to estimate the local mean velocity profile. Coupling between the velocity at hub-height based on this localized top–down model and a wake model is achieved by enforcing a minimum least-square-error in mean velocity in each cell. The wake model in the present implementation takes into account variations in wind farm inflow velocity and represents the wake profile behind each turbine as a super-Gaussian function that smoothly transitions between a top-hat shape in the region immediately following the turbine to a Gaussian profile downstream. Detailed comparisons to large-eddy simulation (LES) data from two different wind farms demonstrate the efficacy of the model in accurately predicting both wind farm power output and local turbine hub-height velocity for different wind farm geometries. These validations using data generated from two different LES codes demonstrate the model's versatility with respect to capturing results from different simulation setups and wind farm configurations.

49 EE - Wind and Water Power Program - Wind (EE-4W↗
DOE OSTI2022

Deploying a Model Predictive Traffic Signal Control Algorithm - A Field Deployment Experiment Case Study

This paper presents a field deployment experiment of a real-time traffic signal control algorithm. We implemented the model predictive control (MPC) algorithm based on the virtual phase-link (VPL) model. We selected the deployment locations and times based on an energy saving potential concept. We developed a set of experiment systems, which included sensing, processing, and actuating components, to enable field deployment. We tested the systems rigorously before the experiment days. We reported the key procedures on the experiment days, including the steps taken, the real-time control procedure, and the monitoring of the experiment. We evaluated the impact of the deployment by looking at the changes in delay and energy consumption.

deployment↗
DOE OSTI2021

C3PU:Real-Time Predictive Charge Control Software for Battery Management Systems (Code for Charge Control and Predictive Unit) [SWR-21-48]

C3PU sets up a model predictive control formulation for the optimal charging control of battery packs and can be deployed for real-time operation on an embedded microprocessor. C3PU uses electrochemical and thermal models of battery packs to predict the charging trajectories of the battery pack over a time horizon under different operating conditions. It then selects the optimal charging trajectory such that a pre-defined objective is minimized, which in the present state of the code is to minimize the charging duration of the battery pack. The optimal charging trajectory is obtained by solving an underlying mathematical problem. However, C3PU is flexible to incorporate other charging objectives. The underlying battery models can be swapped as well, as long as they follow certain mathematical properties. Mathematically, optimal control problems (in this case, optimally controlling the charging current of the battery pack) are computationally expensive. C3PU implements advanced numerical techniques, namely pseudo-spectral optimization, to reduce the computational burden of the underlying problem to solve. This allows for the problem to be solved in real-time in an embedded system. Once the optimal charging trajectory is computed over a time horizon, C3PU can package such data and send it out via appropriate communication protocols.

Mishra, Partha↗
DOE OSTI2022

Language models for the prediction of SARS-CoV-2 inhibitors

The COVID-19 pandemic highlights the need for computational tools to automate and accelerate drug design for novel protein targets. We leverage deep learning language models to generate and score drug candidates based on predicted protein binding affinity. We pre-trained a deep learning language model (BERT) on ∼9.6 billion molecules and achieved peak performance of 603 petaflops in mixed precision. Our work reduces pre-training time from days to hours, compared to previous efforts with this architecture, while also increasing the dataset size by nearly an order of magnitude. For scoring, we fine-tuned the language model using an assembled set of thousands of protein targets with binding affinity data and searched for inhibitors of specific protein targets, SARS-CoV-2 Mpro and PLpro. We utilized a genetic algorithm approach for finding optimal candidates using the generation and scoring capabilities of the language model. Our generalizable models accelerate the identification of inhibitors for emerging therapeutic targets.

Blanchard, Andrew E.↗
DOE OSTI2022

A new dynamic zOnal model with air-diffuser (DOMA) - Application to thermal comfort prediction

A new Dynamic zOnal Model with Air-diffuser (DOMA) was developed. Several case studies were investigated and tested to evaluate and validate this program using measurement data. This new model was integrated into a TRaNsient SYstems Simulation program library and coupled with the multi-zone thermal model. The DOMA/TRNSYS coupled model was then used to predict room temperature distribution over an entire day of a single-zone building. The results show that increasing the heating outputs of the electric floor system, for example, from 75 to 200 W/m 2 , would not effectively improve the indoor thermal comfort, since the thermostat will reach the set point first and then turn off the system before the room gets enough heat and reach a comfortable level. This indicates the importance of selecting an appropriate location and set point for the thermostat when using a floor heating system. This potential thermal comfort issue can only be identified through the two-node model with a dynamic zonal model rather than the conventional PMV model, which thus suggests that for optimizing indoor thermal comfort of a building equipped with a time-sensitive control strategy and/or HVAC system, the TSENS results obtained from the two-node model integrated with DOMA are more appropriate than PMVs.

Construction & Building Technology↗
DOE OSTI2021

History Matching and Performance Prediction of a Polymer Flood Pilot in Heavy Oil Reservoir on Alaska North Slope

The first-ever polymer flood pilot to enhance heavy oil recovery on Alaska North Slope (ANS) is ongoing. After more than 2.5 years of polymer injection, significant benefit has been observed from the decrease in water cut from 65% to less than 15% in the project producers. The primary objective of this study is to develop a robust history-matched reservoir simulation model capable of predicting future polymer flood performance. In this work, the reservoir simulation model has been developed based on the geological model and available reservoir and fluid data. In particular, four high transmissibility strips were introduced to connect the injector-producer well pairs, simulating short-circuiting flow behavior that can be explained by viscous fingering and reproducing the water cut history. The strip transmissibilities were manually tuned to improve the history matching results during the waterflooding and polymer flooding periods, respectively. It has been found that higher strip transmissibilities match the sharp water cut increase very well in the waterflooding period. Then the strip transmissibilities need to be reduced with time to match the significant water cut reduction. The viscous fingering effect in the reservoir during waterflooding and the restoration of injection conformance during polymer flooding have been effectively represented. Based on the validated simulation model, numerical simulation tests have been conducted to investigate the oil recovery performance under different development strategies, with consideration for sensitivity to polymer parameter uncertainties. The oil recovery factor with polymer flooding can reach about 39% in 30 years, twice as much as forecasted with continued waterflooding. Besides, the updated reservoir model has been successfully employed to forecast polymer utilization, a valuable parameter to evaluate the pilot test’s economic efficiency. All the investigated development strategies indicate polymer utilization lower than 3.5 lbs/bbl in 30 years, which is economically attractive.

Wang, Xindan↗
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

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↗
DOE OSTI2023

Evaluating skill in predicting the Interdecadal Pacific Oscillation in initialized decadal climate prediction hindcasts in E3SMv1 and CESM1 using two different initialization methods and a small set of start years

Abstract It is a daunting challenge to conduct initialized hindcasts with enough ensemble members and associated start years to form a drifted climatology from which to compute the anomalies necessary to quantify the skill of the hindcasts when compared to observations. This limits the ability to experiment with case studies and other applications where only a few initial years are needed. Here we run a set of hindcasts with CESM1 and E3SMv1 using two different initialization methods for a limited set of start years and use the respective uninitialized free-running historical simulations to form the model climatologies. Since the drifts from the observed initial states in the hindcasts toward the uninitialized model state are large and rapid, after a few years the drifted initialized models approach the uninitialized model climatological errors. Therefore, hindcasts from the limited start years can use the uninitialized climatology to represent the drifted model states after about lead year 3, providing a means to compute forecast anomalies in the absence of a large hindcast sample. There is comparable skill for predicting spatial patterns of multi-year Pacific sea surface temperature anomalies in the domain of the Interdecadal Pacific Oscillation using this method compared to the conventional methodology with a large hindcast data set, though there is a model dependence to the drifts in the two initialization methods.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Application of the metabolic modeling pipeline in KBase to categorize reactions, predict essential genes, and predict pathways in an isolate genome

The DOE Systems Biology Knowledgebase (KBase) platform offers a range of powerful tools for the reconstruction, refinement, and analysis of genome-scale metabolic models built from microbial isolate genomes. In this chapter, we describe and demonstrate these tools in action with an analysis of isoprene production in the Bacillus subtilis DSM genome. Two different methods are applied to build initial metabolic models for the DSM genome, then the models are gapfilled in three different growth conditions. Next, flux balance analysis (FBA) and flux variability analysis (FVA) techniques are applied to both study the growth of these models in minimal media and classify reactions within each model based on essentiality and functionality. The models are applied with the FBA method to predict essential genes, which are then compared to an updated list of essential genes obtained for B. subtilis 168, a very similar strain to the DSM isolate. The models are also applied to simulate Biolog growth conditions, and these results are compared with Biolog data collected for B. subtilis 168. Finally, the DSM metabolic models are applied to explore the pathways and genes responsible for producing isoprene in this strain. These studies demonstrate the accuracy and utility of models generated from the KBase pipelines, as well as exploring the tools available for analyzing these models.

DOE knowledgebase↗
DOE OSTI2021

A microstructure-based modeling approach to predict the mechanical properties of Zr alloy with hydride precipitates

In nuclear reactors, hydrides can form in fuel cladding due to hydrogen absorption in ZIrcaloy and cause embrittlement. This work presents a microstructure-based finite element model to predict the stress-strain response of Zircaloy containing hydrides. Quantitative microstructural details extracted from scanning electron microscopy (SEM) images were used to generate heterogeneous microstructures including the morphology and spatial distribution of hydrides. The constitutive material model for zircaloy in this study is based on crystal plasticity theory which considers the hexagonal close-packed (HCP) atomic structure of Zircaloy material. The hydrides were modeled as brittle material along with a damage model. Hydride formation inside the zircoloy matrix results in residual stress. This phenomenon is also captured in this model. A parametric study has been conducted to understand the effect of volume fraction, orientation, and lamellae thickness of the hydride phase on the mechanical properties of the overall material.

Kulkarni, Shank S.↗
DOE OSTI2022

Anomalies and Local Structure of Liquid Water from Boiling to the Supercooled Regime as Predicted by the Many-Body MB-pol Model

For the past 50 years, researchers have sought molecular models that can accurately reproduce water’s microscopic structure and thermophysical properties across broad ranges of its complex phase diagram. Herein, molecular dynamics simulations with the many-body MB-pol model are performed to monitor the thermodynamic response functions and local structure of liquid water from the boiling point down to deeply supercooled temperatures at ambient pressure. The isothermal compressibility and isobaric heat capacity show maxima near 223 K, in excellent agreement with recent experiments, and the liquid density exhibits a minimum at ~208 K. A local tetrahedral arrangement, where each water molecule accepts and donates two hydrogen bonds, is found to be the most probable hydrogen-bonding topology at all temperatures. Further, this work suggests that MB-pol may provide predictive capability for studies of liquid water’s physical properties across broad ranges of thermodynamic states, including the so-called water’s “no man’s land” which is difficult to probe experimentally.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Kinetic Modeling of Secondary Organic Aerosol in a Weather-Chemistry Model: Parameterizations, Processes, and Predictions for GOAmazon

Secondary organic aerosol (SOA) forms and evolves in the atmosphere through many pathways and processes, over diverse spatial and time scales. Hence, there is a need to represent these widely-varying kinetic processes in large-scale atmospheric models to allow for accurate predictions of the abundance, properties, and impacts of SOA. In this work, we integrated a kinetic, process-level model (simpleSOM-MOSAIC) into a weather-chemistry model (WRF-Chem) to simulate the oxidation chemistry and microphysics of atmospheric SOA. simpleSOM-MOSAIC simulates multigenerational gas-phase chemistry, autoxidation reactions, heterogeneous oxidation, oligomerization, and phase-state-influenced gas/particle partitioning of SOA. As a case study, the integrated WRF-Chem-simpleSOM-MOSAIC (WC-SSM) model was used to simulate the photochemical evolution downwind of a large city (Manaus, Brazil) in the Amazon and, in turn, study the anthropogenic and biogenic interactions in an otherwise pristine environment. Consistent with previous work, we found that OA was enhanced by up to a factor of four in the urban plume due to elevated hydroxyl radical (OH) concentrations, relative to the background, and that this OA was dominated by SOA from biogenic precursors (80%). Further, in addition to accurately simulating the OA enhancement in the urban plume, the model reproduced the magnitude of the OA oxygen-to-carbon (O:C) ratio and broadly tracked the evolution of the aerosol size distribution. Our work highlights the importance of including an integrated, kinetic representation of SOA processes in an atmospheric model

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Application of the Metabolic Modeling Pipeline in KBase to Categorize Reactions, Predict Essential Genes, and Predict Pathways in an Isolate Genome

The DOE Systems Biology Knowledgebase (KBase) platform offers a range of powerful tools for the reconstruction, refinement, and analysis of genome-scale metabolic models built from microbial isolate genomes. In this chapter, we describe and demonstrate these tools in action with an analysis of isoprene production in the Bacillus subtilis DSM genome. Two different methods are applied to build initial metabolic models for the DSM genome, then the models are gapfilled in three different growth conditions. Next, flux balance analysis (FBA) and flux variability analysis (FVA) techniques are applied to both study the growth of these models in minimal media and classify reactions within each model based on essentiality and functionality. The models are applied with the FBA method to predict essential genes, which are then compared to an updated list of essential genes obtained for B. subtilis 168, a very similar strain to the DSM isolate. The models are also applied to simulate Biolog growth conditions, and these results are compared with Biolog data collected for B. subtilis 168. Finally, the DSM metabolic models are applied to explore the pathways and genes responsible for producing isoprene in this strain. These studies demonstrate the accuracy and utility of models generated from the KBase pipelines, as well as exploring the tools available for analyzing these models.

Allen, Benjamin↗