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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Internal consistency and diversity scenario development: A comparative framework to evaluate energy model scenarios

Energy modeling frameworks and scenario analysis help us explore the potential impact of our actions and uncertainties in future energy systems. Despite their importance, there is no systematic procedure for evaluating the scenario development process. In a literature review, we identify two core elements of the scenario development process: internal consistency and diversity which are oftentimes missing from scenarios. Here, to address this gap, we create the Internal consistency and Diversity Scenario Development (IDSD) comparative framework which aims to assess the feasibility and diversity of scenarios for a given energy model. With this framework, we review commonly used energy models and demonstrate our framework on their scenarios. The IDSD comparative framework can serve several purposes absent from previous scenario development work by aiding energy modelers and report writers in crafting high-quality scenarios. First, the IDSD is a reflective tool which can improve the quality of the scenario development process, enabling a comparative assessment of energy models and scenarios. Second, the IDSD can provide guidance to modeling frameworks with existing scenarios and those still in development; this feedback will enable modelers to improve the development and the communication of the limitations of their scenarios. Third, this study has highlighted areas for improvement in the scenario development of some commonly used energy model frameworks. Finally, there is a complete lack of explanation regarding the stakeholder selection process. Addressing these identified items could increase opportunities for advanced energy technology uptake and improve our options for achieving a more resilient energy system.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

A Unified Scaling Framework for Comparative Analysis of Major Electrical Machine Topologies

This paper presents a unified framework for comparing major electrical machine topologies under identical output and thermal constraints, with emphasis on supply-chain-aware selection among rare-earth-intensive, reduced-rare-earth, and rare-earth-free solutions. Using power factor and air-gap flux density as the principal descriptors, the framework links topology choice to relative size, copper demand, magnet dependence, cost sensitivity, and inertia. To support robust early-stage screening, the deterministic scaling model is combined with uncertainty representation, Monte Carlo scenario propagation, and hesitationaware ranking. The results show that rare-earth-rich machines remain compact and dense, whereas reduced-rare-earth and rare-earth-free alternatives become more attractive under specific material-risk and cost scenarios.

Kumar, Praveen [ORNL] (ORCID:0000000291877857)↗
DOE OSTI2025

Sensitive Detection of Structural Differences using a Statistical Framework for Comparative Crystallography

Chemical and conformational changes underlie the functional cycles of proteins. Comparative crystallography can reveal these changes over time, over ligands, and over chemical and physical perturbations in atomic detail. A key difficulty, however, is that the resulting observations must be placed on the same scale by correcting for experimental factors. We recently introduced a Bayesian framework for correcting (scaling) X-ray diffraction data by combining deep learning with statistical priors informed by crystallographic theory. To scale comparative crystallography data, we here combine this framework with a multivariate statistical theory of comparative crystallography. By doing so, we find strong improvements in the detection of protein dynamics, element-specific anomalous signal, and the binding of drug fragments.

Hekstra, Doeke R. [Harvard Univ., Cambridge, MA (U↗
DOE OSTI2023

Rooftop unit comparison calculator: a framework for comparing performance of rooftop units with building energy simulation

The applications of building energy simulation (BES) in designing heating, ventilation, and air conditioning (HVAC) systems are limited by the high costs of developing simulation models and the lack of references for determining the model parameters. This paper presents a software framework for selecting designs for rooftop unit HVAC (RTU) systems with BES. Specifically, this framework reduces the cost of using BES by automating the generation of EnergyPlus models. It also employs a systematic method for determining model parameters based on well-accepted datasets. We applied this framework in a comprehensive assessment of an advanced design of RTU systems in which 478 EnergyPlus models were developed without human involvement. The assessment reveals that replacing a constant-speed fan/coil with a multiple-speed fan/coil may not guarantee better overall performance. In conclusion, it also suggests the benefits of replacing furnace coils with heat pumps are subject to utility cost, weather conditions, and heating load profiles.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Sensitive detection of structural dynamics using a statistical framework for comparative crystallography

Chemical and conformational changes are crucial to protein function and its pharmacological control. X-ray crystallography can reveal these changes in atomic detail, but standard analysis methods, which refine separate datasets, often overlook differences that are subtle or arise in only a subset of molecules. Direct comparison of crystallographic datasets is, in principle, more powerful, but systematic errors (“scales”) often mask changes in the crystallographic observables (“structure factors”). Machine learning algorithms that jointly estimate scales and structure factors can address this limitation. Here, we augment this approach with multivariate, structured priors derived from crystallographic theory, implemented in the variational deep learning framework Careless. Doing so strongly improves the detection of protein dynamics, element-specific anomalous signals, and the binding of drug candidates, offering a robust approach to comparative crystallography and, potentially, to detection of protein dynamics by other structure determination methods.

Hekstra, Doeke R. [Harvard Univ., Cambridge, MA (U↗
DOE OSTI2024

An Entropy-Based Test and Development Framework for Uncertainty Modeling in Level-Set Visualizations

We present a simple comparative framework for testing and developing uncertainty modeling in uncertain marching cubes implementations. The selection of a model to represent the probability distribution of uncertain values directly influences the memory use, run time, and accuracy of an uncertainty visualization algorithm. We use an entropy calculation directly on ensemble data to establish an expected result and then compare the entropy from various probability models, including uniform, Gaussian, histogram, and quantile models. Our results verify that models matching the distribution of the ensemble indeed match the entropy. We further show that fewer bins in nonparametric histogram models are more effective whereas large numbers of bins in quantile models approach data accuracy.

Sisneros, Robert↗
DOE OSTI2026

Evaluating lightweight unsupervised online IDS for masquerade attacks in CAN

Vehicular controller area networks (CANs) are susceptible to masquerade attacks by malicious adversaries. In masquerade attacks, adversaries silence a targeted ID and then send malicious frames with forged content at the expected timing of benign frames. As masquerade attacks could seriously harm vehicle functionality and are the stealthiest attacks to detect in CAN, recent work has devoted attention to compare frameworks for detecting masquerade attacks in CAN. However, most existing works report offline evaluations using CAN logs already collected using simulations that do not comply with the domain’s real-time constraints. Here we contribute to advance the state of the art by presenting a comparative evaluation of four different non-deep learning (DL)-based unsupervised online intrusion detection systems (IDS) for masquerade attacks in CAN. Our approach differs from existing comparative evaluations in that we analyze the effect of controlling streaming data conditions in a sliding window setting. In doing so, we use realistic masquerade attacks being replayed from the ROAD dataset. We show that although evaluated IDS are not effective at detecting every attack type, the method that relies on detecting changes in the hierarchical structure of clusters of time series produces the best results at the expense of higher computational overhead. We discuss limitations, open challenges, and how the evaluated methods can be used for practical unsupervised online CAN IDS for masquerade attacks.

Anomaly detection↗
DOE OSTI2023

Specificity in plant-mycorrhizal fungal relationships: prevalence, parameterization, and prospects

Species interactions exhibit varying degrees of specialization, ranging from generalist to specialist interactions. For many interactions (e.g., plant-microbiome) we lack standardized metrics of specialization, hindering our ability to apply comparative frameworks of specificity across niche axes and organismal groups. Here, we discuss the concept of plant host specificity of arbuscular mycorrhizal (AM) fungi and ectomycorrhizal (EM) fungi, including the predominant theories for their interactions: Passenger, Driver, and Habitat Hypotheses. We focus on five major areas of interest in advancing the field of plant-mycorrhizal fungal host specificity: phylogenetic specificity, host physiology specificity, functional specificity, habitat specificity, and mycorrhizal fungal-mediated plant rarity. Considering the need to elucidate foundational concepts of specificity in this globally important symbiosis, we propose standardized metrics and comparative studies to enhance our understanding. We also emphasize the importance of analyzing global mycorrhizal data holistically to draw meaningful conclusions and suggest a shift toward single-species analyses to unravel the complexities underlying these associations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Discovering Innovations in Stress Tolerance through Comparative Gene Regulatory Network Analysis and Cell-Type Specific Expression Maps (Final Technical Report with Cover Page)

Through this grant, we developed a comparative framework to elucidate the mechanisms behind variations in environmental stress responses among a diverse group of species within the Brassicaceae family. Our focus was on the differences in physiological and transcriptomic responses to abscisic acid (ABA), a hormone associated with water stress. We examined the differential growth responses of four Brassicaceae species, finding that most exhibited reduced root growth correlated with smaller meristem size. In contrast, Schrenkiella parvula showed accelerated growth due to increased root cell elongation. We employed RNA sequencing to analyze the transcriptional responses to ABA across these species, and innovative bioinformatics techniques were used to pinpoint biological pathways with significant divergence. Additionally, we utilized DAP-seq to map the gene regulatory networks associated with ABAresponsive transcription factors, revealing that variations in the regulation of growth hormone biosynthesis play a critical role in the distinct ABA effects on root growth among the species. This research sets a new standard for comparative physiology by integrating comparative genomics and transcriptomics to uncover pathway divergences.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRSDate not supplied

Sensorimotor Application of Proposed Methods to Combine the Effects of Multiple Countermeasures for PRisM

Risk associated with human systems is challenging to quantify but is critical for the mission planning and decision making required to enable future Lunar and Martian missions. To address this gap, the Crew Health and Performance-Probabilistic Risk Assessment (CHP-PRA) project is developing an integrated computational model for CHP mission risk. Much like how MEDPRAT is designed to allow medical resource trades informed by medical risk, CHP-PRA will enable analogous trades in human system risks across multiple CHP functions and capabilities. Human performance is one component of the risk intended to be captured by CHP-PRA through the Performance Risk Model (PRisM). The sensorimotor risk area provides a good frame of reference for investigating the structure of a performance model because most tasks that astronauts are expected to perform require input from the sensory system and/or movement/motor control. Additionally, sensorimotor countermeasures are an area of particular concern for NASA’s Human Research Program because of the increased sensorimotor risk associated with surface operations in Lunar and Martian missions. Thus, a tool that can quickly compare risk reductions of potential countermeasures would be beneficial in guiding research and development of effective countermeasures. In this proof of concept, we present a systematic way to combine multiple performance data sets for humans subjected to different countermeasures such that we can predict the countermeasure(s) that optimize astronaut performance on relevant tasks. PRisM assumes that both the tests that are used to measure countermeasure effectiveness (input data) and the tasks we use to represent astronaut performance, can be broken down and represented as a function/vector of the human systems required to perform that test/task. Through mathematical combination, test data are used to predict performance on astronaut tasks that use similar systems. We propose that when combining countermeasures evaluated using the same test, that only one value should be used to represent their combined effectiveness. We start our analysis with the assumption that two countermeasures together will perform better than each countermeasure individually. Our initial implementation of this framework compares various space motion sickness countermeasures and the most up to date analysis will be demonstrated at the IWS.

Caroline R Austin↗
DOE OSTI2025

Hybrid Doping Strategy with High‐Entropy Cu/Fe Surface Modification and Zr Bulk Incorporation for Ni‐Rich Cathodes

A hybrid doping strategy combining Zr 4+ bulk doping with high-entropy Cu 2+ /Fe 3+ surface doping is developed to enhance the structural and interfacial stability of Ni-rich layered oxide cathodes. Cu and Fe are selectively introduced at the particle surface via a surface-selective ion-exchange process, forming a ≈15 nm Fe-rich layer while preserving the layered framework. Compared to the pristine cathode, the hybrid sample exhibits significantly improved electrochemical performance in both half-cell and full-cell configurations. In half-cells, the hybrid retains 88.5% and 90.2% after 100 cycles at 1C under 4.6 and 4.5 V, respectively. During high-voltage full-cell cycling, the hybrid cathode maintains over 80% capacity retention, whereas the pristine counterpart retains less than 10% under identical conditions over the same cycling period. XPS, EELS, and DEMS analyses confirm improved oxygen retention, suppressed gas evolution, and stable surface chemistry, while DFT calculations indicate enhanced Me–O bonding in the selected Fe 0.75 Cu 0.25 (Mn 1/16 Co 2/16 Ni 13/16 )O 2 surface composition, which is identified through DFT-calculated mixing energy reaching a minimum at this ratio, indicating the most thermodynamically favorable configuration. In conclusion, these results demonstrate the effectiveness of this hybrid doping strategy in mitigating coupled degradation pathways in Ni-rich cathodes.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Physics-informed neural networks for heterogeneous poroelastic media

This study presents a novel physics-informed neural network (PINN) framework for modeling poroelasticity in heterogeneous media with material interfaces. The approach introduces a composite neural network (CoNN) where separate neural networks predict displacement and pressure variables for each material. While sharing identical activation functions, these networks are independently trained for all other parameters. To address challenges posed by heterogeneous material interfaces, the CoNN is integrated with the Interface-PINNs (I-PINNs) framework (Sarma et al., Comput. Methods Appl. Mech. Eng. 429: 117135, 2024), allowing different activation functions across material interfaces. Further, this ensures accurate approximation of discontinuous solution fields and gradients. Performance and accuracy of this combined architecture were evaluated against the conventional PINNs approach, a single neural network (SNN) architecture, and the eXtended PINNs (XPINNs) framework through two one-dimensional benchmark examples with discontinuous material properties. The results show that the proposed CoNN with I-PINNs architecture achieves an RMSE that is two orders of magnitude better than the conventional PINNs approach and is at least 40 times faster than the SNN framework. Compared to XPINNs, the proposed method achieves an RMSE at least one order of magnitude better and is 40% faster.

42 ENGINEERING↗
DOE OSTI2023

Non-asymptotic analysis of ensemble Kalman updates: effective dimension and localization

Many modern algorithms for inverse problems and data assimilation rely on ensemble Kalman updates to blend prior predictions with observed data. Ensemble Kalman methods often perform well with a small ensemble size, which is essential in applications where generating each particle is costly. This paper develops a non-asymptotic analysis of ensemble Kalman updates, which rigorously explains why a small ensemble size suffices if the prior covariance has moderate effective dimension due to fast spectrum decay or approximate sparsity. Here, we present our theory in a unified framework, comparing everal implementations of ensemble Kalman updates that use perturbed observations, square root filtering and localization. As part of our analysis, we develop new dimension-free covariance estimation bounds for approximately sparse matrices that may be of independent interest.

Mathematics↗
NASA NTRS1995

Fossilization Processes in Thermal Springs

To create a comparative framework for the study of ancient examples, we have been carrying out parallel studies of the microbial biosedimentology, taphonomy and geochemistry of modem and sub-Recent thermal spring deposits. One goal of the research is the development of integrated litho- and taphofacies models for siliceous and travertline sinters. Thermal springs are regarded as important environments for the origin and early evolution of life on Earth, and we seek to utilize information from the fossil record to reconstruct the evolution of high temperature ecosystems. Microbial contributions to the fabric of thermal spring sinters occur when population growth rates keep pace with, or exceed rates of inorganic precipitation, allowing for the development of continuous biofilms or mats. In siliceous thermal springs, microorganisms are typically entombed while viable. Modes of preservation reflect the balance between rates of organic matter degradation, silica precipitation and secondary infilling. Subaerial sinters are initially quite porous and permeable and at temperatures higher than about 20 C, organic materials are usually degraded prior to secondary infilling of sinter frameworks. Thus, organically-preserved microfossils are rare and fossil information consists of characteristic biofabrics formed by the encrustation and underplating of microbial mat surfaces. This probably accounts for the typically low total organic carbon values observed in thermal spring deposits. In mid-temperature, (approx. 35 - 59 C) ponds and outflows, the surface morphology of tufted Phormidium mats is preserved through mat underplating by thin siliceous: crusts. Microbial taxes lead to clumping of ceils and/or preferred filament orientations that together define higher order composite fabrics in thermal spring stromatolites (e.g. network, coniform, and palisade). At lower temperatures (less than 35 C), Calothrix mats cover shallow terracette pools forming flat carpets or pustular surfaces that produce palisade and "shrub" fabrics, respectively. At finer scales, composite fabrics are seen to consist distinctive associations of microstructures formed by the encrustation of individual cells and filaments. Composite fabrics survive the diagenetic transitions from primary opaline silica to quartz and are known from subaerial thermal spring deposits as old as Lower Carboniferous. However, fossil microorganisms tend to be rare in older deposits, and are usually preserved only where cells or sheaths have been stained by iron oxides. In subaqueous mineralizing springs at lower temperatures, early infilling leads to a more rapid and complete reduction in porosity and permeability. This process, along with the slower rates of microbial degradation at lower temperatures, creates a more favorable situation for organic matter preservation. Application of this taphonomic model to the Rhynie Chert, previously interpreted as subaerial, suggest it was probably deposited in a subaqueous spring setting at lower temperatures.

Farmer, Jack D.↗
NASA NTRS2002

The origin and evolution of model organisms

The phylogeny and timescale of life are becoming better understood as the analysis of genomic data from model organisms continues to grow. As a result, discoveries are being made about the early history of life and the origin and development of complex multicellular life. This emerging comparative framework and the emphasis on historical patterns is helping to bridge barriers among organism-based research communities.

Review, Academic↗
NASA NTRS2010

A Mixed Integer Linear Program for Solving a Multiple Route Taxi Scheduling Problem

Aircraft movements on taxiways at busy airports often create bottlenecks. This paper introduces a mixed integer linear program to solve a Multiple Route Aircraft Taxi Scheduling Problem. The outputs of the model are in the form of optimal taxi schedules, which include routing decisions for taxiing aircraft. The model extends an existing single route formulation to include routing decisions. An efficient comparison framework compares the multi-route formulation and the single route formulation. The multi-route model is exercised for east side airport surface traffic at Dallas/Fort Worth International Airport to determine if any arrival taxi time savings can be achieved by allowing arrivals to have two taxi routes: a route that crosses an active departure runway and a perimeter route that avoids the crossing. Results indicate that the multi-route formulation yields reduced arrival taxi times over the single route formulation only when a perimeter taxiway is used. In conditions where the departure aircraft are given an optimal and fixed takeoff sequence, accumulative arrival taxi time savings in the multi-route formulation can be as high as 3.6 hours more than the single route formulation. If the departure sequence is not optimal, the multi-route formulation results in less taxi time savings made over the single route formulation, but the average arrival taxi time is significantly decreased.

Montoya, Justin Vincent↗