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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Data-driven estimation of energy consumption for electric bus under real-world driving conditions

Reliable and accurate estimation of an electric bus’s instantaneous energy consumption is critical in evaluating energy impacts of planning and control of electric bus operations. In this study, we developed machine learning-based long short-term memory (LSTM) and artificial neural network (ANN) models to estimate 1 Hz energy consumption of electric buses based on continuous monitoring data of electric buses in Chattanooga, Tennessee, in 2019 and 2020. We propose a data-partitioning algorithm to separate energy charging and discharging modes before applying data-driven estimation models. Here, a K-fold cross-validation-based model selection process was conducted to identify the optimal model structure and input variables in terms of prediction accuracy. The estimation results show the predicted mean absolute percentage error rates of LSTM and ANN models were 3% and 5%, respectively. We compared the proposed models with existing models in the literature based on the same testing data to demonstrate the predictability of our models.

Artificial neural network↗
DOE OSTI2024

Progressive transfer learning for advancing machine learning-based reduced-order modeling

Abstract To maximize knowledge transfer and improve the data requirement for data-driven machine learning (ML) modeling, a progressive transfer learning for reduced-order modeling (p-ROM) framework is proposed. A key concept of p-ROM is to selectively transfer knowledge from previously trained ML models and effectively develop a new ML model(s) for unseen tasks by optimizing information gates in hidden layers. The p-ROM framework is designed to work with any type of data-driven ROMs. For demonstration purposes, we evaluate the p-ROM with specific Barlow Twins ROMs (p-BT-ROMs) to highlight how progress learning can apply to multiple topological and physical problems with an emphasis on a small training set regime. The proposed p-BT-ROM framework has been tested using multiple examples, including transport, flow, and solid mechanics, to illustrate the importance of progressive knowledge transfer and its impact on model accuracy with reduced training samples. In both similar and different topologies, p-BT-ROM achieves improved model accuracy with much less training data. For instance, p-BT-ROM with four-parent (i.e., pre-trained models) outperforms the no-parent counterpart trained on data nine times larger. The p-ROM framework is poised to significantly enhance the capabilities of ML-based ROM approaches for scientific and engineering applications by mitigating data scarcity through progressively transferring knowledge.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Securing Grid-interactive Efficient Buildings (GEB) through Cyber Defense and Resilient System (CYDRES)

The DOE CYDRES project is driven by the urgent need to address critical research gaps in the domain of cyber-physical security of smart buildings, including Grid-interactive Efficient Buildings (GEBs). CYDRES, a real-time advanced building resilient platform, aims to enhance the cyber-attack-immune capabilities of buildings through multi-layered prevention, detection, and adaptation mechanisms. CYDRES consists of five key modules: a multi-layer network analyzer, an Automatic Fault Detection, Diagnosis, and Prognosis (AFDDP) framework, an intelligent mode selector, a cyber-resilient control framework, and a situation awareness platform. The Network Analyzer employs a data-driven framework that includes a protocol state learning tool and a CRF (Conditional Random Field) command validator. In Hardware-In-the-Loop (HIL) testbeds, it achieved 100% detection accuracy with a false alarm rate of 3%, validating its efficacy in identifying selected cyber-attacks. The AFDDP framework leverages pattern matching, PCA (Principal Component Analysis)-based strategies, and a DBN (Dynamic Bayesian Network)-based fault diagnosis approach to pinpoint the causes of physical system abnormalities using Building Automation System (BAS) data. In HIL experiments, the AFDDP module attained a detection accuracy of over 95% with a false alarm rate below 7%. Additionally, the fault detector utilized machine learning (Random Forest) and deep learning (Multi-Layer Perceptron) methods with acoustic sensor data to achieve a 100% fault detection accuracy in Heating, Ventilation, and Air-Conditioning (HVAC) equipment. The Mode Selector offered real-time impact analysis, allowing immediate actions to protect BASs in the face of emerging threats. The cyber-resilient control framework included an adaptive Model Predictive Control (MPC) and a measurement compensator, reducing temperature violations by up to 94% and improving the total demand flexibility by up to 70% in HIL experiments. Such HIL experiments covered a cyber-attack case and a physical fault case, showcasing CYDRES’ efficiency in maintaining operational continuity during threats. The situation awareness platform in Grafana enhanced real-time threat detection and response visualization, augmenting the operational awareness for building operators. CYDRES demonstrated high technical effectiveness in various test scenarios, particularly in HIL environments. The project's phased development approach ensured efficient use of resources, highlighting its practical feasibility and readiness for commercialization. By enhancing the security and resilience of building operations, CYDRES represents a significant advance in mitigating risks associated with cyber-physical systems, thereby enhancing public confidence in the safety of modern building infrastructure. Future directions for the project include expanding testing protocols, refining AFDDP methodologies, exploring more comprehensive resilient control strategies, and testing in real commercial buildings.

42 ENGINEERING↗
DOE OSTI2020

Flow-based likelihoods for non-Gaussian inference

We investigate the use of data-driven likelihoods to bypass a key assumption made in many scientific analyses, which is that the true likelihood of the data is Gaussian. In particular, we suggest using the optimization targets of flow-based generative models, a class of models that can capture complex distributions by transforming a simple base distribution through layers of nonlinearities. We call these flow-based likelihoods (FBL). We analyze the accuracy and precision of the reconstructed likelihoods on mock Gaussian data, and show that simply gauging the quality of samples drawn from the trained model is not a sufficient indicator that the true likelihood has been learned. We nevertheless demonstrate that the likelihood can be reconstructed to a precision equal to that of sampling error due to a finite sample size. We then apply FBLs to mock weak lensing convergence power spectra, a cosmological observable that is significantly non-Gaussian (NG). We find that the FBL captures the NG signatures in the data extremely well, while other commonly used data-driven likelihoods, such as Gaussian mixture models and independent component analysis, fail to do so. This suggests that works that have found small posterior shifts in NG data with data-driven likelihoods such as these could be underestimating the impact of non-Gaussianity in parameter constraints. By introducing a suite of tests that can capture different levels of NG in the data, we show that the success or failure of traditional data-driven likelihoods can be tied back to the structure of the NG in the data. Here, unlike other methods, the flexibility of the FBL makes it successful at tackling different types of NG simultaneously. Because of this, and consequently their likely applicability across datasets and domains, we encourage their use for inference when sufficient mock data are available for training.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Toward Wireless Smart Grid Communications: An Evaluation of Protocol Latencies in an Open-Source 5G Testbed

Fifth-generation networks promise wide availability of wireless communication with inherent security features. The 5G standards also outline access for different applications requiring low latency, machine-to-machine communication, or mobile broadband. These networks can be advantageous to numerous applications that require widespread and diverse communications. One such application is found in smart grids. Smart grid networks, and Operational Technology (OT) networks in general, utilize a variety of communication protocols for low-latency control, data monitoring, and reporting at every level. Transitioning these network communications from wired Wide Area Networks (WANs) to wireless communication through 5G can provide additional benefits to their security and network configurability. However, introducing these wireless capabilities may also result in a degradation of network latency. In this paper, we propose utilizing 5G for smart grid communications, and we evaluate the latency impacts of encapsulating GOOSE, Modbus, and DNP3 for transmission over a 5G network. The OpenAirInterface open-source library is utilized to deploy an in-lab 5G Core Network and gNB for testing with off-the-shelf User Equipment (UE). This creates an effective 5G test platform for experimenting with different OT protocols such as GOOSE. The results are validated by measuring two different Intelligent Electronic Devices’ contact closure times for each network configuration. These tests are also conducted for varying packet sizes in order to isolate different sources of network latency. Our study outlines the latency impact of communication over 5G for time-critical and non-critical applications regarding their transition toward private 5G-based OT network implementations. The conducted experiments illustrate that in the case of GOOSE packets, simple encapsulation may exceed the protocol’s time-critical nature, and, therefore, additional measures must be taken to ensure a viable transition of GOOSE to 5G services. However, non-critical applications are shown to be viable for migration to 5G.

42 ENGINEERING↗
DOE OSTI2020

The PAU Survey: Photometric redshifts using transfer learning from simulations

In this paper, we introduce the DEEPZ deep learning photometric redshift (photo-z) code. As a test case, we apply the code to the PAU survey (PAUS) data in the COSMOS field. DEEPZ reduces the σ68 scatter statistic by 50 percent at iAB = 22.5 compared to existing algorithms. This improvement is achieved through various methods, including transfer learning from simulations where the training set consists of simulations as well as observations, which reduces the need for training data. The redshift probability distribution is estimated with a mixture density network (MDN), which produces accurate redshift distributions. Our code includes an autoencoder to reduce noise and extract features from the galaxy SEDs. It also benefits from combining multiple networks, which lowers the photo-z scatter by 10 percent. Furthermore, training with randomly constructed coadded fluxes adds information about individual exposures, reducing the impact of photometric outliers. In addition to opening up the route for higher redshift precision with narrow bands, these machine learning techniques can also be valuable for broad-band surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

A Genome-Based Model to Predict the Virulence of Pseudomonas aeruginosa Isolates

ABSTRACT: Variation in the genome of Pseudomonas aeruginosa , an important pathogen, can have dramatic impacts on the bacterium’s ability to cause disease. We therefore asked whether it was possible to predict the virulence of P. aeruginosa isolates based on their genomic content. We applied a machine learning approach to a genetically and phenotypically diverse collection of 115 clinical P. aeruginosa isolates using genomic information and corresponding virulence phenotypes in a mouse model of bacteremia. We defined the accessory genome of these isolates through the presence or absence of accessory genomic elements (AGEs), sequences present in some strains but not others. Machine learning models trained using AGEs were predictive of virulence, with a mean nested cross-validation accuracy of 75% using the random forest algorithm. However, individual AGEs did not have a large influence on the algorithm’s performance, suggesting instead that virulence predictions are derived from a diffuse genomic signature. These results were validated with an independent test set of 25 P. aeruginosa isolates whose virulence was predicted with 72% accuracy. Machine learning models trained using core genome single-nucleotide variants and whole-genome k-mers also predicted virulence. Our findings are a proof of concept for the use of bacterial genomes to predict pathogenicity in P. aeruginosa and highlight the potential of this approach for predicting patient outcomes. IMPORTANCE Pseudomonas aeruginosa is a clinically important Gram-negative opportunistic pathogen. P. aeruginosa shows a large degree of genomic heterogeneity both through variation in sequences found throughout the species (core genome) and through the presence or absence of sequences in different isolates (accessory genome). P. aeruginosa isolates also differ markedly in their ability to cause disease. In this study, we used machine learning to predict the virulence level of P. aeruginosa isolates in a mouse bacteremia model based on genomic content. We show that both the accessory and core genomes are predictive of virulence. This study provides a machine learning framework to investigate relationships between bacterial genomes and complex phenotypes such as virulence.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Combining Machine Learning and Comparative Effectiveness Methodology to Study Primary Care Pharmacotherapy Pathways for Veterans With Depression

Our objective is to demonstrate an innovative method combining machine learning with comparative effectiveness research techniques and to investigate a hitherto unstudied question about the effectiveness of common prescribing patterns. For Operation Enduring Freedom/Operation Iraqi Freedom veterans with major depressive disorder, we generate pharmacotherapy pathways (of antidepressants) using process mining and machine learning. We select the medication episodes that were started at subtherapeutic doses by the first assigned primary care physician and observe the paths that those medication episodes follow. Using 2-stage least squares, we test the effectiveness of starting at a low dose and staying low for longer versus ramping up fast while balancing observable and unobservable characteristics of patients and providers through instrumental variables. We leverage predetermined provider practice patterns as instruments. We collected outpatient pharmacy data for selective serotonin reuptake inhibitors and selective norepinephrine reuptake inhibitors, patient and provider characteristics (as control variables), and the instruments for our cohort. All data were extracted for the period between 2006 and 2020. There is a statistically significant positive effect (0.68, 95% CI 0.11–1.25) of “ramping up fast” on engagement in care. When we examine the effect of “ramping up slow”, we see an insignificant negative impact on engagement in care (−0.82, 95% CI −1.89 to 0.25). As expected, the probability of drop-out also seems to have a negative effect on engagement in care (−0.39, 95% CI −0.94 to 0.17). We further validate these results by testing with medication possession ratios calculated periodically as an alternative engagement in care metric. Our findings contradict the “Start low, go slow” adage, indicating that ramping up the dose of an antidepressant faster has a significantly positive effect on engagement in care for our population.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

An Investigation into the Surface Skidding Response of PBX 9501 and PBX 9502

While polymer-bonded explosives, PBX, have reduced sensitivity to ignition from mechanical shock or heating compared to conventional explosives, the investigation of the mechanical ignition mechanisms for PBX remains vital to assessing the safety during machining and general handling. Under frictional heating, hot spot generation due to high melting point contaminants in the interface is a suspected source for increased probability of detonation. Here, to investigate such frictional heating, samples of PBX 9501 and PBX 9502 were impacted and skidded against a tangentially moving target and thermally imaged. Temporally resolved temperature and forces were simultaneously measured with and without 300 µm silica grit at the frictional interface. A trend of increasing temperature was observed as the speed and tangential force on the sample increased. Grit particles in the frictional interface were found to act as frictional heat concentration spots owing to the grit's protrusion from the surface and lesser susceptibility to melting compared to the PBX. The coefficient of friction between PBX 9501 and the skidding surface was observed to be largely constant for forces and speeds at the lower end of the test range. In contrast, at high tangential speeds, the coefficient was found to be significantly lower.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Cyberattack Detection and Mitigation on Central Volt‐VAr Using Circuit Law and Machine Learning

ABSTRACT In a distribution grid, voltage is maintained within a nominal range through a Volt‐VAr function that controls capacitor banks, reactive power of distributed energy resources (DER), and on‐load tap changers (OLTC). Availability of communications helps with the implementation of central Volt‐VAr control; however, it also opens the system to cyberattacks, causing voltage disturbances. Previous work has shown the adverse impacts of false data injection (FDI) on the central Volt‐VAr control; however, very few works have studied methods to detect and mitigate FDI on Volt‐VAr control. This paper addresses gaps in the detection and mitigation of FDI on the measurement packets of a central Volt‐VAr control. This work uses a two‐stage algorithm for cyberattack detection since the accuracy of a single‐stage machine learning (ML)–based detection method decreases while dealing with unseen data. The first stage is based on the verification of measurements against circuit laws, and the second stage utilizes a tree search algorithm and an ML method to detect the falsified data. This paper compares long short‐term memory (LSTM) and bidirectional LSTM (BiLSTM) as the employed ML algorithms. Finally, the mitigation algorithm replaces the falsified data with the estimated output of the ML algorithm. The effectiveness of the proposed method is tested for several cases using the IEEE 13‐bus test system in PSCAD software.

Beikbabaei, Milad [Bradley Department of Electrica↗
DOE OSTI2021

Multi-cyclone analysis and machine learning model implications of cyclone effects on forests

Past studies of cyclones (hurricanes, typhoons, tropical cyclones) disturbance showed that meteorological, topographical, and biological factors affect the patterns of forest disturbance intensity but left open the extent to which these findings were representative across different global cyclone regions. Using remote sensing data and machine learning models, we examined how these factors change over spatial scales and assessed their consistency across four major cyclones: Katrina (August 2005), Rita (September 2005), Yasi (February 2011), and María (September 2017). Our results revealed that the factors which best explained forest disturbance intensity pattern varied across these regions. Wind speed and precipitation were the dominant factors contributing to the variation in impacts of Katrina; terrain features, especially elevation, explained most of the variation in disturbance intensity of Rita; pre-disturbance vegetation condition was significant predictors of effects of Yasi; these factors played equal roles in explaining the disturbance intensity variation of María. A 40 m/s (144 km/h) wind speed threshold was proposed to split low- and high-level forest disturbance intensity. Other than wind speed, few generalizations can be made on features across multiple regions. We built several generalized hurricane impact models, which worked well with the test data from cyclones used for model development (R 2 = 0.89). However, these models did not have good predictions on other cyclones, such as Michael (October 2018) and Laura (August 2020). This study showed that each cyclone interacted with the landscape in a unique way and the challenges remained in building a generalized cyclone impact model.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Machine Learning Framework for Characterizing Processing–Structure Relationship in Block Copolymer Thin Films

The morphology of block copolymers (BCPs) critically influences material properties and applications. This work introduces a machine learning (ML)-enabled, high-throughput framework for analyzing grazing incidence small-angle X-ray scattering (GISAXS) data and atomic force microscopy (AFM) images to characterize BCP thin film morphology. A convolutional neural network was trained to classify AFM images by surface features, achieving 97% testing accuracy. Classified images were then analyzed to extract 2D grain size measurements from the samples in a high-throughput manner. ML models were trained to predict domain orientation based on processing parameters such as solvent ratio, additive type, and additive ratio. GISAXS-based properties were predicted with strong performances (R 2 > 0.75), while AFM-based property predictions were less accurate (R 2 < 0.60), likely due to the localized nature of AFM measurements compared to the bulk information captured by GISAXS. Beyond model performance, interpretability was addressed using SHapley Additive exPlanations (SHAP). SHAP analysis revealed that the additive ratio had the largest impact on morphological predictions, where additive provides the BCP chains with increased volume to rearrange into thermodynamically favorable morphologies. This interpretability helps validate model predictions and offers insight into parameter importance. Altogether, the presented framework combining high-throughput characterization and interpretable ML offers an approach to exploring and optimizing BCP thin film morphology across a broad processing landscape.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Overview of IMPACT Data Acquisition System and Data Reduction Process

This report documents the development of the data acquisition system (DAS) and data reduction methodologies for the Irradiated Material Property Accelerated Characterization Test (IMPACT) experiment at the Advanced Test Reactor (ATR). The IMPACT experiment is designed to enable in-pile measurement of thermal conductivity in metallic nuclear fuels, specifically U-10Zr, using an instrumented thermal conductivity probe. The DAS supports both passive temperature monitoring and active thermal interrogation of the probe through controlled AC and DC excitation. Significant modifications to laboratory-scale systems were required to accommodate the higher resistance paths associated with the in-pile application. Custom electronics and relay-controlled measurement sequencing were developed to enable the measurement and sufficient power delivery to the sensing region. A reduced-order, axisymmetric thermal model based on the thermal quadrupoles method is presented to support data interpretation. This model enables efficient evaluation of transient heat transfer behavior and facilitates solution of the inverse problem required to extract thermal properties from measured signals. Multiple boundary condition formulations are discussed to address varying experimental time scales and geometries. Additionally, machine learning techniques are introduced to support data reduction and improve confidence in inverse solutions. Convolutional neural networks are applied to identify the presence of gas gaps and other evolving geometric features that significantly impact thermal response during irradiation. These efforts contribute to the broader integration of digital twin frameworks and real-time modeling capabilities within the Advanced Fuels Campaign.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2023

Melt pool instability detection using coaxial photodiode system validated by in-situ X-ray imaging

A co-axial photodiode monitoring system with high temporal resolution has been integrated into a proven test bench enabling synchronized side-view high speed X-ray imaging of melt pool dynamics and top-view spectral emission characterization of the melt pool. Here, this setup enables direct observation of melt pool phenomena and correlation between the two monitoring systems which can be directly scaled to commercial systems. The work demonstrates a 92% detection rate in keyhole collapse phenomena related to defect generation in SLM. Furthermore, the impact of gas flow on monitoring signals is studied to understand the fundamental importance of gas flow in commercial systems.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Optimization of Triply Periodic Minimal Surface Heat Exchanger to Achieve Compactness, High Efficiency, and Low-Pressure Drop

With advancements in additive manufacturing (AM) techniques, high-quality triply periodic minimal surface (TPMS) structures can now be produced. TPMS walled heat exchangers (HX) hold significant potential for industrial applications and are receiving increasing attention. This paper explores the impact of various TPMS design variables on flow and thermal performance to optimize TPMS heat exchangers for compactness, high efficiency, and low pressure drop. The design variables examined include the type of TPMS lattice, unit cell size, wall thickness, aspect ratio, TPMS orientation, and equivalent thickness. The study reveals that the flow and heat transfer performance of TPMS structures are significantly affected by these design variables. For the Gyroid, Diamond, and SplitP lattices, performance is nearly identical when the surface-to-volume ratio is kept constant. The average velocity of the fluid in the TPMS HX should be 0.3 m/s. The corresponding Re is between 300~800. Thin wall thickness, small equivalent thickness, and flat lattice configurations can significantly reduce pressure drop while maintaining the overall heat transfer coefficient. Additionally, the angle between the flow direction and TPMS orientation can increase pressure drop. Three aluminum heat exchangers were successfully printed using an AM machine, and testing results are comparable with theoretical prediction.

42 ENGINEERING↗
DOE OSTI2020

Gaining Perspective on Unconventional Well Design Choices through Play-level Application of Machine Learning Modeling

The recent development of unconventional oil and gas (O&G) reservoirs has led to an abundant hydrocarbon supply, both domestically and globally. However, there is a continued push to develop new and innovative approaches to improve exploration and extraction efficiencies and overall well productivity moving forward. Substantial improvements in unconventional O&G development are expected through optimized well completion and stimulation strategies aimed at maximizing well productivity. Optimizing well designs will require tailoring to the distinctive geologic conditions present for any newly placed well. To better evaluate the impact of well design attributes and their associated interactions on productivity in a major unconventional play, multivariate machine learning-based models that use empirical datasets were developed. A gradient boosted regression tree (GBRT) algorithm was applied. GBRT has been narrowly investigated for O&G applications but enables straightforward parametric importance and influence evaluation, as well as assessment of parameter interaction effects. Models were trained on well design and locational parameters that serve as a proxy for variable geologic conditions to estimate two types of productivity indicator response variables strongly correlated to estimated ultimate recovery (EUR). The dataset utilized consists of over 7,000 well observations that cover the majority of the productive region of the Marcellus Shale. Model performance was evaluated and algorithm parameters tuned by analyzing the goodness-of-fit for simulated results against observed data in a cross-validation approach. Models were found capable of 73–79 percent prediction accuracy on held out testing data of gas equivalent production and can be used to inform future well design and placement decisions for increasing EUR per well and improving overall field-level recovery. Study results indicate that Marcellus well performance improves most with upscaling perforated interval lengths and water and proppant volumes per foot; but relative productivity improvements are spatially dependent across the play. Finally, optimal combinations of water and proppant on well performance were found to vary depending on well location, emphasizing the utility of data-driven models capable of broad application across a play of interest for informing tailored well design approaches prior to their field deployment.

04 OIL SHALES AND TAR SANDS↗
DOE OSTI2025

Demonstration of a code coupling framework for modeling beam-collimator impacts in the advanced photon source

The high-brightness beams being produced in current and future accelerators present new machine protection concerns with the potential for high-energy-density (HED) conditions ( >100 J/mm 3 ) in beam-intercepting components. Simulating HED conditions in accelerators requires utilizing a suite of physics codes for particle dynamics, particle-matter interactions, and hydrodynamics. This paper describes a method of coupling the codes elegant, fluka, and flash to simulate the effects of a rapid beam loss in the advanced photon source storage ring and the resulting interaction of the beam and collimators. This paper expands previous work [J. Dooling et al., Collimator irradiation studies at the advanced photon source, in Proceedings of the IBIC-2023 (2023), pp. 245–249] by introducing a definition of the evolving geometry of the collimator surface as well as providing methods for simulating the absorption of synchrotron radiation and tracking shower particles produced during beam strikes. We demonstrate this framework by simulating machine conditions of the APS ring before and after its recent upgrade. Simulation results are compared with observed damage to collimators and test samples taken from the APS ring.

Accelerator/storage ring control systems↗
DOE OSTI2026

Improving thermodynamic nudging in the E3SM Atmosphere Model version 2 (EAMv2): strategy and hindcast skills on weather systems

Nudging techniques are commonly employed to constrain atmospheric simulations toward observed states, facilitating model evaluation and sensitivity studies. However, if applied improperly – particularly to thermodynamic variables such as temperature and humidity – nudging can distort physical processes and introduce spurious biases, undermining the credibility of the simulations. This study presents an improved nudging implementation that applies vertically modulated tendencies to reduce adverse impacts on model physics. The framework is tested in version 2 of the Energy Exascale Earth System Model (EAMv2) using a suite of hindcast simulations nudged toward ERA5 reanalysis. We systematically evaluate the individual and combined effects of nudging wind, temperature, and humidity fields on the model's ability to represent large-scale atmospheric states and high-impact weather systems. Results show that the revised strategy – particularly when nudging temperature and humidity at selected levels – enhances hindcast skill by improving agreement with ERA5 without degrading the hydrological cycle or precipitation processes. Additional improvements in surface temperature, outgoing longwave radiation, and precipitation biases are achieved through targeted nudging of land-surface variables. The proposed approach strengthens the representation of large-scale conditions relevant to tropical cyclones, atmospheric rivers, and extratropical cyclones in the low-resolution EAMv2. These findings demonstrate that carefully designed thermodynamic nudging, especially of temperature and humidity, improves the realism of constrained simulations and broadens the utility of nudged EAMv2 for atmospheric modeling, machine learning, and high-impact weather research.

Atmospheric river↗