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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

A rat retinal damage model predicts for potential clinical visual disturbances induced by Hsp90 inhibitors

In human trials certain heat shock protein 90 (Hsp90) inhibitors, including 17-DMAG and NVP-AUY922, have caused visual disorders indicative of retinal dysfunction; others such as 17-AAG and ganetespib have not. To understand these safety profile differences we evaluated histopathological changes and exposure profiles of four Hsp90 inhibitors, with or without clinical reports of adverse ocular effects, using a rat retinal model. Retinal morphology, Hsp70 expression (a surrogate marker of Hsp90 inhibition), apoptotic induction and pharmacokinetic drug exposure analysis were examined in rats treated with the ansamycins 17-DMAG and 17-AAG, or with the second-generation compounds NVP-AUY922 and ganetespib. Both 17-DMAG and NVP-AUY922 induced strong yet restricted retinal Hsp70 up-regulation and promoted marked photoreceptor cell death 24 h after the final dose. In contrast, neither 17-AAG nor ganetespib elicited photoreceptor injury. When the relationship between drug distribution and photoreceptor degeneration was examined, 17-DMAG and NVP-AUY922 showed substantial retinal accumulation, with high retina/plasma (R/P) ratios and slow elimination rates, such that 51% of 17-DMAG and 65% of NVP-AUY922 present at 30 min post-injection were retained in the retina 6 h post-dose. For 17-AAG and ganetespib, retinal elimination was rapid (90% and 70% of drugs eliminated from the retina at 6 h, respectively) which correlated with lower R/P ratios. These findings indicate that prolonged inhibition of Hsp90 activity in the eye results in photoreceptor cell death. Moreover, the results suggest that the retina/plasma exposure ratio and retinal elimination rate profiles of Hsp90 inhibitors, irrespective of their chemical class, may predict for ocular toxicity potential. - Highlights: • In human trials some Hsp90 inhibitors cause visual disorders, others do not. • Prolonged inhibition of Hsp90 in the rat eye results in photoreceptor cell death. • Retina/plasma ratio and retinal elimination rate are linked to toxicity potential. • Rat retinotoxic responses to individual Hsp90 inhibitors reflect clinical profiles. • Rodent modeling may be used to assess ocular risks of targeted Hsp90 compounds.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Molecular Modeling Predicts Novel Antibody Escape Mutations in the Respiratory Syncytial Virus Fusion Glycoprotein

Respiratory syncytial virus (RSV) causes severe disease in young infants, particularly those with heart or lung diseases or born prematurely. Because no vaccine is currently available, monoclonal antibodies are used to prevent severe RSV disease in high-risk infants. While it is known that RSV evolves to avoid recognition by antibodies, screening tools that can predict which changes to the virus may lead to antibody resistance are greatly needed.

molecular modeling↗
DOE OSTI2019

Inclusion Capture Probability Prediction Model for Bubble Floatation in Turbulent Steel Flow

The turbulent flow around bubbles is simulated for the fixed turbulent kinetic energy. Inclusion motion through the flow field is studied by considering the stochastic effect of turbulence to calculate the capture probability of inclusions with different sizes to a fixed-size individual bubble according to the redefinition. Using the cubic spline interpolation, the capture probability is predicted as functions of the bubble size, inclusion size and turbulent kinetic energy.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Gene-informed decomposition model predicts lower soil carbon loss due to persistent microbial adaptation to warming

Abstract Soil microbial respiration is an important source of uncertainty in projecting future climate and carbon (C) cycle feedbacks. However, its feedbacks to climate warming and underlying microbial mechanisms are still poorly understood. Here we show that the temperature sensitivity of soil microbial respiration ( Q 10 ) in a temperate grassland ecosystem persistently decreases by 12.0 ± 3.7% across 7 years of warming. Also, the shifts of microbial communities play critical roles in regulating thermal adaptation of soil respiration. Incorporating microbial functional gene abundance data into a microbially-enabled ecosystem model significantly improves the modeling performance of soil microbial respiration by 5–19%, and reduces model parametric uncertainty by 55–71%. In addition, modeling analyses show that the microbial thermal adaptation can lead to considerably less heterotrophic respiration (11.6 ± 7.5%), and hence less soil C loss. If such microbially mediated dampening effects occur generally across different spatial and temporal scales, the potential positive feedback of soil microbial respiration in response to climate warming may be less than previously predicted.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Stability of Ensemble Models Predicts Productivity of Enzymatic Systems

Stability in a metabolic system may not be obtained if incorrect amounts of enzymes are used. Without stability, some metabolites may accumulate or deplete leading to the irreversible loss of the desired operating point. Even if initial enzyme amounts achieve a stable steady state, changes in enzyme amount due to stochastic variations or environmental changes may move the system to the unstable region and lose the steady-state or quasi-steady-state flux. This situation is distinct from the phenomenon characterized by typical sensitivity analysis, which focuses on the smooth change before loss of stability. Here we show that metabolic networks differ significantly in their intrinsic ability to attain stability due to the network structure and kinetic forms, and that after achieving stability, some enzymes are prone to cause instability upon changes in enzyme amounts. We use Ensemble Modelling for Robustness Analysis (EMRA) to analyze stability in four cell-free enzymatic systems when enzyme amounts are changed. Loss of stability in continuous systems can lead to lower production even when the system is tested experimentally in batch experiments. The predictions of instability by EMRA are supported by the lower productivity in batch experimental tests. Finally, the EMRA method incorporates properties of network structure, including stoichiometry and kinetic form, but does not require specific parameter values of the enzymes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Force Reconstruction from Ejection Tests of Stores from Aircraft Used for Model Predictions and Missing/Bad Gages

One of the more severe environments for a store on an aircraft is during the ejection of the store. During this environment it is not possible to instrument all component responses, and it is also likely that some instruments may fail during the environment testing. This work provides a method for developing these responses from failed gages and uninstrumented locations. First, the forces observed by the store during the environment are reconstructed. A simple sampling method is used to reconstruct these forces given various parameters. Then, these forces are applied to a model to generate the component responses. Validation is performed on this methodology.

42 ENGINEERING↗
DOE OSTI2025

Toward a Generalizable Prediction Model of Molten Salt Mixture Density with Chemistry‐Informed Transfer Learning

Optimally designing applications of molten salts requires knowledge of their thermophysical properties over a wide range of temperatures and compositions. There exist significant gaps in existing databases and this data can be challenging to experimentally measure due to high temperatures, salt corrosivity, and salt hygroscopicity. Existing databases have been used to create Redlich–Kister (RK) models for mixture density showing improved accuracy with respect to ideal mixing assumptions, but these models require subcomponent data measurements for each new system, therefore lacking generality. In order to address generalizability and data sparsity, a transfer learning procedure is proposed to train deep neural networks (DNNs) using a combination of semi‐empirical relationships (RK), data from the thermophysical arm of the molten salt thermal properties database and universal ab initio properties of component mixtures taken from the joint automated repository for various integrated simulations (JARVIS) classical force‐field inspired descriptors database to predict density in molten salts. Herein, it is shown that DNNs predict molten salt density with an r 2 over 0.99 and a mean absolute percentage error under 1%, outperforming alternative methods.

inorganic materials↗
DOE OSTI2016

A Multi-Compartment Hybrid Computational Model Predicts Key Roles for Dendritic Cells in Tuberculosis Infection

Tuberculosis (TB) is a world-wide health problem with approximately 2 billion people infected with Mycobacterium tuberculosis (Mtb, the causative bacterium of TB). The pathologic hallmark of Mtb infection in humans and Non-Human Primates (NHPs) is the formation of spherical structures, primarily in lungs, called granulomas. Infection occurs after inhalation of bacteria into lungs, where resident antigen-presenting cells (APCs), take up bacteria and initiate the immune response to Mtb infection. APCs traffic from the site of infection (lung) to lung-draining lymph nodes (LNs) where they prime T cells to recognize Mtb. These T cells, circulating back through blood, migrate back to lungs to perform their immune effector functions. We have previously developed a hybrid agent-based model (ABM, labeled GranSim) describing in silico immune cell, bacterial (Mtb) and molecular behaviors during tuberculosis infection and recently linked that model to operate across three physiological compartments: lung (infection site where granulomas form), lung draining lymph node (LN, site of generation of adaptive immunity) and blood (a measurable compartment). Granuloma formation and function is captured by a spatio-temporal model (i.e., ABM), while LN and blood compartments represent temporal dynamics of the whole body in response to infection and are captured with ordinary differential equations (ODEs). In order to have a more mechanistic representation of APC trafficking from the lung to the lymph node, and to better capture antigen presentation in a draining LN, this current study incorporates the role of dendritic cells (DCs) in a computational fashion into GranSim.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Predictive Model and Methodology for Heat Treatment Distortion Final Report CRADA No. TC-298-92

This project was a multi-lab, multi-partner CRADA involving LLNL, Los Alamos National Laboratory, Sandia National Laboratories, Oak Ridge National Laboratory, Martin Marietta Energy Systems and the industrial partner, The National Center of Manufacturing Sciences (NCMS). A number of member companies of NCMS participated including General Motors Corporation, Ford Motor Company, The Torrington Company, Gear Research, the Illinois Institute of Technology Research Institute, and Deformation Control Technology •. LLNL was the lead laboratory for metrology technology used for validation of the computational tool/methodology. LLNL was also the lead laboratory for the development of the software user interface , for the computational tool. This report focuses on the participation of LLNL and NCMS. The purpose of the project was to develop a computational tool/methodology that engineers would use to predict the effects of heat treatment on the _size and shape of industrial parts made of quench hardenable alloys. Initially, the target application of the tool was gears for automotive power trains.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A novel methodology to integrate outcomes regarding perioperative pain experience into a composite score: Prediction model development and validation

Abstract Background An integrated score that globally assesses perioperative pain experience and rationally weights each component has not yet been developed. Methods A development dataset specific to adult Chinese patients undergoing orthopaedic surgery was obtained from PAIN OUT (1985 qualified patients of 2244). A more recent validation dataset obeying the same conditions was obtained from the Chinese Anaesthesia Shared‐database Platform (1004 qualified patients of 1032). Outcomes were assessed using the International Pain Outcomes Questionnaire (IPO‐Q), which comprises key patient‐level outcomes of perioperative pain management, including pain experience and perceptions of care. Using principal component analysis and regression models, a composite score (CS) was inferred to integrate pain experience. The discrimination of the CS for dissatisfaction and desire for more pain treatment was compared with that of the worst pain score. Results A CS was developed from the 12 items of the IPO‐Q regarding pain experience. The weight for calculating the CS was worst pain 11, least pain 17, time spent in severe pain 11, interference with activity in bed 9, interference with breathing deeply or coughing 10, interference with sleep 9, anxiety 12, helplessness 12, nausea 0, drowsiness 2, itch 5 and dizziness 2. In external validation, the CS indicated superior discrimination to the worst pain in predicting dissatisfaction ( p < 0.001) and desire for more pain treatment ( p < 0.001). Conclusions This study introduced a methodology to integrate outcomes regarding perioperative pain experience into a CS, which was based on the weight of each item. Significance This novel methodology sheds additional light on the riveting issue of carefully integrating several measures into a composite endpoint, which may be useful for quality improvement purposes when addressing the impact of a change in clinical practice.

Jiang, Bailin↗
DOE OSTI2015

Standard Model Prediction for Direct C P Violation in K → π π Decay

In this paper, we report the first lattice QCD calculation of the complex kaon decay amplitude A 0 with physical kinematics, using a 32 3 x 64 lattice volume and a single lattice spacing a, with 1/a = 1.3784(68) GeV. We find Re(A 0 ) = 4.66(1.00)(1.26) x 10 -7 GeV and Im(A 0 ) = -1.90(1.23)(1.08) x 10 -11 GeV, where the first error is statistical and the second systematic. The first value is in approximate agreement with the experimental result: Re(A 0 ) = 3.3201(18) x 10 -7 GeV, while the second can be used to compute the direct CP-violating ratio Re(ϵ'/ϵ) = 1.38(5.15)(4.59) x 10 -4 , which is 2.1σ below the experimental value 16.6(2.3) x 10 -4 . Finally, the real part of A 0 is CP conserving and serves as a test of our method while the result for Re(ϵ'/ϵ) provides a new test of the standard model theory of CP violation, one which can be made more accurate with increasing computer capability.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Digital Twinning and Predictive Modeling of Traffic for Safe, Efficient, and Reliable Intersections

Over the last decade, the advances in connected and autonomous vehicles (CAVs) have far surpassed the technological realm of transportation infrastructure. There is a growing need to have a technologically commensurate transportation infrastructure to enable safe and reliable movement of goods and people. The recent creation of Advanced Research Projects Agency - Infrastructure (ARPA-I) through the Infrastructure Investment and Jobs Act by the U.S. Department of Transportation (USDOT) has further amplified the need to revolutionize the transportation infrastructure system in the US. This need is perhaps felt most at traffic intersections, as more than 50 percent of the combined total of fatal and injury crashes occur at or near intersections. The proposed concept of Infrastructure Perception and Control (IPC) is aimed at bridging this technological gap by building a real-time digital twin of traffic by fusing detections from sensors installed at the intersection. This digital twin can then empower a wide variety of applications such as smart traffic signals or infrastructure-to-everything (I2X) communications. Smart signaling can help avoid crashes through early-prediction, while I2X can augment the CAV sensors under uncertain driving conditions and provide connected vehicles (CVs) with traffic information they can use to optimize their travel.

connected vehicles↗
DOE OSTI2025

Catalytic Fast Pyrolysis Oil Stand-Alone and Co-Hydroprocessing Case Studies: Design, Cost, and Sustainability Based on Process Model Predictions

Refined liquid fuels provide conveniences for road, rail, air, and marine transportation due to high energy density, ease of transport and storage, and existing production and distribution infrastructure. Biomass conversion technologies can help diversify and increase the supply of liquid fuels towards a more robust energy future. The backbone of such development must include reliable, high-volume biomass supply chains and efficient utilization of those resources. These activities, if pursued, will lead to rural infrastructure development and new jobs.

09 BIOMASS FUELS↗
DOE OSTI2015

Failure Mode Classification for Life Prediction Modeling of Solid-State Lighting

Since the passing of the Energy Independence and Security Act of 2007, the U.S. government has mandated greater energy independence which has acted as a catalyst for accelerating and facilitating research efforts toward the development and deployment of market-driven solutions for energy-saving homes, buildings and manufacturing, as well as sustainable transportation and renewable electricity generation. As part of this effort, an emphasis toward advancing solid-state lighting technology through research, development, demonstration, and commercial applications is assisting in the phase out of the common incandescent light bulb, as well as developing a more economical lighting source that is less toxic than compact fluorescent lighting. This has led lighting manufacturers to pursue SSL technologies for a wide range of consumer lighting applications. An SSL luminaire’s lifetime can be characterized in terms of lumen maintenance life. Lumen maintenance or lumen depreciation is the percentage decrease in the relative luminous flux from that of the original, pristine luminous flux value. Lumen maintenance life is the estimated operating time, in hours, when the desired failure threshold is projected to be reached at normal operating conditions. One accepted failure threshold of SSL luminaires is lumen maintenance of 70% -- a 30% reduction in the light output of the luminaire. Currently, the only approved lighting standard that puts forth a recommendation for long-term luminous flux maintenance projections towards a specified failure threshold of an SSL luminaire is the IES TM-28-14 (TM28) standard. iii TM28 was derived as a means to compare luminaires that have been tested at different facilities, research labs or companies. TM28 recommends the use of the Arrhenius equation to determine SSL device specific reaction rates from thermally driven failure mechanisms used to characterize a single failure mode – the relative change in the luminous flux output or “light power” of the SSL luminaire. The use of the Arrhenius equation necessitates two different temperature conditions, 25°C and 45°C are suggested by TM28, to determine the SSL lamp specific activation energy. One principal issue with TM28 is the lack of additional stresses or parameters needed to characterize non-temperature dependent failure mechanisms. Another principal issue with TM28 is the assumption that lumen maintenance or lumen depreciation gives an adequate comparison between SSL luminaires. Additionally, TM28 has no process for the determination of acceleration factors or lifetime estimations. Currently, a literature gap exists for established accelerated test methods for SSL devices to assess quality, reliability and durability before being introduced into the marketplace. Furthermore, there is a need for Physics-of-Failure based approaches to understand the processes and mechanisms that induce failure for the assessment of SSL reliability in order to develop generalized acceleration factors that better represent SSL product lifetime. This and the deficiencies in TM28 validate the need behind the development of acceleration techniques to quantify SSL reliability under a variety of environmental conditions. The ability to assess damage accrual and investigate reliability of SSL components and systems is essential to understanding the life time of the SSL device itself. The methodologies developed in this work increases the understanding of SSL devices iv through the investigation of component and device reliability under a variety of accelerated test conditions. The approaches for suitable lifetime predictions through the development of novel generalized acceleration factors, as well as a prognostics and health management framework, will greatly reduce the time and effort needed to produce SSL acceleration factors for the development of lifetime predictions.

Sakalaukus, Peter Joseph↗
DOE OSTI2015

Computational Tools for Predictive Modeling of Properties in Complex Actinide Systems

In this chapter we focus on methodological and computational aspects that are key to accurately modeling the spectroscopic and thermodynamic properties of molecular systems containing actinides within the density functional theory (DFT) framework. Our focus is on properties that require either an accurate relativistic all-electron description or an accurate description of the dynamical behavior of actinide species in an environment at finite temperature, or both. The implementation of the methods and the calculations discussed in this chapter were done with the NWChem software suite (Valiev et al. 2010). In the first two sections we discuss two methods that account for relativistic effects, the ZORA and the X2C Hamiltonian. Section 1.2.1 discusses the implementation of the approximate relativistic ZORA Hamiltonian and its extension to magnetic properties. Section 1.3 focuses on the exact X2C Hamiltonian and the application of this methodology to obtain accurate molecular properties. In Section 1.4 we examine the role of a dynamical environment at finite temperature as well as the presence of other ions on the thermodynamics of hydrolysis and exchange reaction mechanisms. Finally, Section 1.5 discusses the modeling of XAS (EXAFS, XANES) properties in realistic environments accounting for both the dynamics of the system and (for XANES) the relativistic effects.

actinide↗