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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 1,351 records · Page 75
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Prevalence and Predictors of Neoadjuvant Therapy for Stage IIIA Non-Small Cell Lung Cancer in the National Cancer Database: Importance of Socioeconomic Status and Treating Institution

Purpose: The optimal locoregional therapy for stage IIIA non-small cell lung cancer (NSCLC) is controversial, with definitive chemoradiation therapy (CRT) and neoadjuvant therapy followed by surgery (NT-S) serving as competing strategies. In this study, we used the National Cancer Database to determine the prevalence and predictors of NT in a large, modern cohort of patients. Methods and Materials: Patients with stage IIIA NSCLC treated with CRT or NT-S between 2003 and 2010 at programs accredited by the Commission on Cancer were included. Predictors were categorized as clinical, time/geographic, socioeconomic, and institutional. In accord with the National Cancer Database, institutions were classified as academic/research program and as comprehensive and noncomprehensive community cancer centers. Logistic regression and random effects multilevel logistic regression were performed for univariable and multivariable analyses, respectively. Results: The cohort consisted of 18,581 patients, 3,087 (16.6%) of whom underwent NT-S (10.6% induction CRT, 6% induction chemotherapy). The prevalence of NT-S was constant over time, but there were significant relative 31% and 30% decreases in pneumonectomy and right-sided pneumonectomy, respectively, over time (P trend <.02). In addition to younger age, lower T stage, and favorable comorbidity score, indicators of higher socioeconomic status were strong independent predictors of NT-S, including white race, higher income, and private/managed insurance. The type of institution (academic/research program vs comprehensive or noncomprehensive community cancer centers, odds ratio 1.54 and 2.08, respectively) strongly predicted NT-S, but treatment volume did not. Conclusions: Neoadjuvant therapy followed by surgery was an uncommon treatment approach in Commission on Cancer programs, and the prevalence of postinduction pneumonectomy decreased over time. Higher socioeconomic status and treatment at academic institutions were significant predictors of NT-S. Further research should be performed to enable a better understanding of these disparities.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

Phase 1 Trial of Bevacizumab With Concurrent Chemoradiation Therapy for Squamous Cell Carcinoma of the Head and Neck With Exploratory Functional Imaging of Tumor Hypoxia, Proliferation, and Perfusion

Purpose: A phase 1 trial was completed to examine the safety and feasibility of combining bevacizumab with radiation and cisplatin in patients with locoregionally advanced squamous cell carcinoma of the head and neck (HNSCC) treated with curative intent. Additionally, we assessed the capacity of bevacizumab to induce an early tumor response as measured by a series of biological imaging studies. Methods and Materials: All patients received a single induction dose of bevacizumab (15 mg/kg) delivered 3 weeks (±3 days) before the initiation of chemoradiation therapy. After the initial dose of bevacizumab, comprehensive head and neck chemoradiation therapy was delivered with curative intent to 70 Gy in 33 fractions with concurrent weekly cisplatin at 30 mg/m{sup 2} and bevacizumab every 3 weeks (weeks 1, 4, 7) with dose escalation from 5 to 10 to 15 mg/kg. All patients underwent experimental imaging with [{sup 18}F]fluorothymidine positron emission tomography (FLT-PET) (proliferation), [{sup 61}Cu]Cu-diacetyl-bis(N4-methylthiosemicarbazone) PET (Cu-ATSM-PET) (hypoxia), and dynamic contrast-enhanced computed tomography (DCE-CT) (perfusion) at 3 time points: before bevacizumab monotherapy, after bevacizumab monotherapy, and during the combined therapy course. Results: Ten patients were enrolled. All had stage IV HNSCC, all achieved a complete response to treatment, and 9 of 10 remain alive, with a mean survival time of 61.3 months. All patients experienced grade 3 toxicity, but no dose-limiting toxicities or significant bleeding episodes were observed. Significant reductions were noted in tumor proliferation (FLT-PET), tumor hypoxia (Cu-ATSM-PET), and DCE-CT contrast enhancement after bevacizumab monotherapy, with further decreases in FLT-PET and Cu-ATSM-PET during the combined therapy course. Conclusions: The incorporation of bevacizumab into comprehensive chemoradiation therapy regimens for patients with HNSCC appears safe and feasible. Experimental imaging demonstrates measureable changes in tumor proliferation, hypoxia, and perfusion after bevacizumab monotherapy and during chemoradiation therapy. These findings suggest opportunities to preview the clinical outcomes for individual patients and thereby design personalized therapy approaches in future trials.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

A proteome-wide assessment of the oxidative stress paradigm for metal and metal-oxide nanomaterials in human macrophages

Responsible implementation of engineered nanomaterials (ENMs) into commercial applications is an important societal issue, driving demand for new approaches for rapid and comprehensive evaluation of their bioactivity and safety. An essential part of any research focused on identifying potential hazard of ENMs is the appropriate selection of biological endpoints to evaluate. Herein, we use a tiered strategy employing both targeted biological assays and untargeted quantitative proteomics to elucidate the biological responses of human macrophages across a library of metal/metal oxide ENMs, raised as priority ENMs for investigation by NIEHS’s Nanomaterial Health Implications Research (NHIR) program. Our results show that quantitative cellular proteome profiles readily distinguish ENM types based on their cytotoxic potential according to induction of biological processes and pathways involved in the cellular antioxidant response, TCA cycle, oxidative stress, endoplasmic reticulum stress, and immune responses as major processes impacted. Interestingly, pathway analysis of differentially abundant proteins also revealed new cell processes that were influenced by all ENMs independent of cytotoxic potential. These included pathways previously implicated as cellular adaptive mechanisms to low levels of oxidative stress, including cell adhesion, protein translation and protein targeting pathways. Unsupervised clustering of all data revealed the most striking proteome changes that differentiated ENM classes involved a small subset of proteins involved in the oxidative stess response (HMOX1), protein chaperone functions (HS71B, DNJB1), and autophagy (SQSTM), providing a potential new panel of markers of ENM-induced cell stress. To our knowledge, the results represent the most comprehensive profiling of the biological responses to a library of ENMs conducted using quantitative mass spectrometry-based proteomics. The results provide a basis to identify the patterns of a diverse set of cellular pathways and cell processes impacted by ENM exposure in an important immune cell type, laying the foundation for multivariate, pathway-level structure activity assessments of ENMs in the future.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Application of community data to surface complexation modeling framework development: Iron oxide protolysis

This study presents a comprehensive community data-driven surface complexation modeling framework for simulating potentiometric titration of mineral surfaces. Compiled community data for ferrihydrite, goethite, hematite, and magnetite are fit to produce representative protolysis constants that can reproduce potentiometric titration data collected from multiple literature sources. Using this framework, the impact of surface complexation model type and surface site density (SSD) on the fit quality and protolysis constants can be readily evaluated. For example, the non-electrostatic model yielded a poor data fit compared to diffuse double layer model and constant capacitance models due to the absence of known surface charge effects. Regardless of the choice of iron oxide mineral, pK a1 decreased with increasing SSD while the opposite tendency was observed for pK a2 . This newly developed framework demonstrates a method to reconcile community data-wide potentiometric titration data using Findable, Accessible, Interoperable, Reusable data principles to produce mineral protolysis constants that improve robustness of surface complexation models for applications in metal sorption and reactive transport modeling. The framework is readily expandable (as community data increase) and extensible (as the number of minerals increase). The framework provides a path forward for developing self-consistent, comprehensive, and updateable surface complexation databases for surface complexation and reactive transport modeling.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Bridging the Energy Future: The Role and Potential of Hydrogen Co-Firing with Natural Gas

In the transition toward sustainable energy systems, the co-firing of hydrogen (H2) with natural gas emerges as a critical technology for cleaner power generation. This study examines its benefits, including emissions reduction, enhanced efficiency, and compatibility with existing infrastructure. Concurrently, it addresses the inherent challenges, such as combustion dynamics, necessary infrastructure modifications, and economic considerations. Incorporating global case studies, the review explores recent advancements in hydrogen production and co-firing, evaluating their practicality and operational efficiency. It also provides a critical examination of the economic implications and societal acceptance, which are crucial for the adoption of hydrogen technologies. Moreover, the review investigates the synergy between hydrogen and renewable energy technologies, assessing potential impacts on energy policy and regulation. Ultimately, this comprehensive analysis underscores the essential role of hydrogen co-firing in the energy sector and advocates for a comprehensive strategy that synergizes technological progress, economic viability, regulatory support, and public engagement to promote a resilient and sustainable energy future.

hydrogen co-firing↗
DOE OSTI2015

Accelerating population balance-Monte Carlo simulation for coagulation dynamics from the Markov jump model, stochastic algorithm and GPU parallel computing

This paper proposes a comprehensive framework for accelerating population balance-Monte Carlo (PBMC) simulation of particle coagulation dynamics. By combining Markov jump model, weighted majorant kernel and GPU (graphics processing unit) parallel computing, a significant gain in computational efficiency is achieved. The Markov jump model constructs a coagulation-rule matrix of differentially-weighted simulation particles, so as to capture the time evolution of particle size distribution with low statistical noise over the full size range and as far as possible to reduce the number of time loopings. Here three coagulation rules are highlighted and it is found that constructing appropriate coagulation rule provides a route to attain the compromise between accuracy and cost of PBMC methods. Further, in order to avoid double looping over all simulation particles when considering the two-particle events (typically, particle coagulation), the weighted majorant kernel is introduced to estimate the maximum coagulation rates being used for acceptance–rejection processes by single-looping over all particles, and meanwhile the mean time-step of coagulation event is estimated by summing the coagulation kernels of rejected and accepted particle pairs. The computational load of these fast differentially-weighted PBMC simulations (based on the Markov jump model) is reduced greatly to be proportional to the number of simulation particles in a zero-dimensional system (single cell). Finally, for a spatially inhomogeneous multi-dimensional (multi-cell) simulation, the proposed fast PBMC is performed in each cell, and multiple cells are parallel processed by multi-cores on a GPU that can implement the massively threaded data-parallel tasks to obtain remarkable speedup ratio (comparing with CPU computation, the speedup ratio of GPU parallel computing is as high as 200 in a case of 100 cells with 10 000 simulation particles per cell). These accelerating approaches of PBMC are demonstrated in a physically realistic Brownian coagulation case. The computational accuracy is validated with benchmark solution of discrete-sectional method. The simulation results show that the comprehensive approach can attain very favorable improvement in cost without sacrificing computational accuracy.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons

Neural networks (NNs) are currently changing the computational paradigm on how to combine data with mathematical laws in physics and engineering in a profound way, tackling challenging inverse and ill-posed problems not solvable with traditional methods. However, quantifying errors and uncertainties in NN-based inference is more complicated than in traditional methods. This is because in addition to aleatoric uncertainty associated with noisy data, there is also uncertainty due to limited data, but also due to NN hyperparameters, overparametrization, optimization and sampling errors as well as model misspecification. Although there are some recent works on uncertainty quantification (UQ) in NNs, there is no systematic investigation of suitable methods towards quantifying the total uncertainty effectively and efficiently even for function approximation, and there is even less work on solving partial differential equations and learning operator mappings between infinite-dimensional function spaces using NNs. In this work, we present a comprehensive framework that includes uncertainty modeling, new and existing solution methods, as well as evaluation metrics and post-hoc improvement approaches. Further, to demonstrate the applicability and reliability of our framework, we present an extensive comparative study in which various methods are tested on prototype problems, including problems with mixed input-output data, and stochastic problems in high dimensions. In the Appendix, we include a comprehensive description of all the UQ methods employed. Further, to help facilitate the deployment of UQ in Scientific Machine Learning research and practice, we present and develop in [1] an open-source Python library (github.com/Crunch-UQ4MI/neuraluq), termed NeuralUQ, that is accompanied by an educational tutorial and additional computational experiments.

11 physics-informed neural networks↗
DOE OSTI2017

Towards Optimization of ACRT Schedules Applied to the Gradient Freeze Growth of Cadmium Zinc Telluride

Historically, the melt growth of II-VI crystals has benefitted by the application of the accelerated crucible rotation technique (ACRT). Here, we employ a comprehensive numerical model to assess the impact of two ACRT schedules designed for a cadmium zinc telluride growth system per the classical recommendations of Capper and co-workers. The “flow maximizing” ACRT schedule, with higher rotation, effectively mixes the solutal field in the melt but does not reduce supercooling adjacent to the growth interface. The ACRT schedule derived for stable Ekman flow, with lower rotation, proves more effective in reducing supercooling and promoting stable growth. Furthermore, these counterintuitive results highlight the need for more comprehensive studies on the optimization of ACRT schedules for specific growth systems and for desired growth outcomes.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Influence of Pt-Metal Alloy Catalysts with Various Ionomers on Oxygen Reduction Reaction in Fuel Cell Application

Pt-M/C (M = Co, Ni, Mn, etc.) alloy catalysts exhibit superior oxygen reduction reaction (ORR) activity compared to pure Pt/C, leading to a high energy efficiency in hydrogen fuel cells. However, many Pt-M/C alloy catalysts were synthesized and evaluated at the lab scale in model test-bed systems like rotating disc electrodes, which don't always correlate to performance within a fuel cell system; there is a clear need to evaluate catalysts in electrodes that can be prepared at industrially relevant scales to evaluate how factors like ink formulation can greatly affect device-level of fuel cell performance. Herein, three commercial Pt-M/C alloy catalysts (two Pt-Co/C and one Pt-Ni/C) were comprehensively characterized by various techniques. The results show that the average particle sizes of the three catalysts are close to 5 nm; the atomic ratio of Pt/M is around 4; and the M was successfully embedded into Pt lattice, resulting in the positive shift of Pt 4f in XPS spectra and XRD patterns. These catalytic materials were incorporated into 9 different cathode catalyst layers (CCLs) with three kinds of ionomers (Nafion D2020, high oxygen permeability ionomer (HOPI), and Aquivion D79-25BS), and their performance in proton exchange membrane fuel cells (PEMFCs) were investigated. The results demonstrate that the Pt-Co/C catalysts possess a higher mass activity (MA) than Pt-Ni/C; the cathodes with Nafion ionomer provide the highest MA while electrodes with Aquivion ionomer showed the lowest activity, attributed to poor H+ conductivity resulting from suboptimal ionomer incorporation. Finally, these alloys were shown to exceed DOE targets for MA and H2/Air performance reported in the recent publications at beginning of life and after 90k cycle catalyst AST protocol. This study provides valuable performance benchmarks for these materials guiding future Pt-M/C catalyst design and material integration for heavy duty PEMFC applications.

08 HYDROGEN↗
DOE OSTI2016

Alteration of natural 37 Ar activity concentration in the subsurface by gas transport and water infiltration

High 37 Ar activity concentration in soil gas is proposed as a key evidence for the detection of underground nuclear explosion by the Comprehensive Nuclear Test-Ban Treaty. However, such a detection is challenged by the natural background of 37 Ar in the subsurface, mainly due to Ca activation by cosmic rays. A better understanding and improved capability to predict 37 Ar activity concentration in the subsurface and its spatial and temporal variability is thus required. A numerical model integrating 37 Ar production and transport in the subsurface is developed, including variable soil water content and water infiltration at the surface. A parameterized equation for 37 Ar production in the first 15 m below the surface is studied, taking into account the major production reactions and the moderation effect of soil water content. Using sensitivity analysis and uncertainty quantification, a realistic and comprehensive probability distribution of natural 37 Ar activity concentrations in soil gas is proposed, including the effects of water infiltration. Site location and soil composition are identified as the parameters allowing for a most effective reduction of the possible range of 37 Ar activity concentrations. The influence of soil water content on 37Ar production is shown to be negligible to first order, while 37 Ar activity concentration in soil gas and its temporal variability appear to be strongly influenced by transient water infiltration events. Overall, these results will be used as a basis for practical CTBTO concepts of operation during an OSI.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2023

Prevalence of Listeria monocytogenes , Salmonella spp., Shiga toxin-producing Escherichia coli , and Campylobacter spp. in raw milk in the United States between 2000 and 2019: A systematic review and meta-analysis

Raw (unpasteurized) milk is available for sale and direct human consumption within some states in the United States (US); it cannot be sold or distributed in interstate commerce. Raw milk may contain pathogenic microorganisms that, when consumed, may cause illness and sometimes may result in death. No comprehensive review for prevalence and levels of the major bacterial pathogens in raw milk in the US exists. The objective of the present research was to systematically review the scientific literature published from 2000 to 2019 to estimate the prevalence and levels of Listeria monocytogenes, Salmonella spp., Shiga toxin-producing Escherichia coli (STEC), and Campylobacter spp. in raw milk in the US. Peer-reviewed studies were retrieved systematically from PubMed®, Embase®, and Web of ScienceTM. The unique complete nonduplicate references were uploaded into the Health Assessment Work Collaborative (HAWC). Based on the selection criteria, twenty studies were included in the systematic review and meta-analysis. Comprehensive Meta-Analysis (CMA) was used for statistical analyses, specifically, random effects meta-analyses were used to synthesize raw bulk tank milk (BTM) and milk filters (MF) data. Data from studies using culture and non–culture-based detection methods were included. Forest plots generated in CMA (Biostat, Englewood, NJ) were used to visualize the results. The average prevalence (event rate) of L. monocytogenes, Salmonella spp., STEC, and Campylobacter spp. in raw BTM in the US was estimated at 4.3% (95% confidence intervals [CIs], 2.8–6.5%), 3.6% (95% CIs, 2.0–6.2%), 4.3% (95% CIs, 2.4–7.4%), and 6.0% (95% CIs, 3.2–10.9%), respectively. Estimated prevalence was generally larger in MF than in BTM. There was not enough data to perform a meta-analysis for the prevalence or levels of pathogens in raw milk from retail establishments or other milk categories.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Density functional theory study of the point defect energetics in y- LiAlO2, Li2ZrO3 and Li2TiO3 materials

The defect energetics in y-LiAlO2, Li2 TiO3, and Li2ZrO3 materials used in tritium-producing burnable absorber rods and fusion solid breeder applications was investigated using density functional theory calculations. A comprehensive analysis of the charged defects was performed for cation and anion vacancies, interstitials, antisite defects, and 3H interstitial and substitutional defects to understand the defect structures at different charge states and their stability as a function of the electron chemical potential across the electronic band gap of crystals at operating temperature and oxygen partial pressure conditions. Through investigation of the local bonding configurations and projected electronic density of states of the 3H related charged defects, including 3H interstitial (3Hint), 3Hint bound with an oxygen interstitial, and substitutional 3H defects on the Li, O, and M (M¼ Al, Ti, and Zr) sites (3HLi, 3HO, and 3HM), the 3H states of these defects were distinguished, i.e. triton (3Hþ), neutral 3H (3H0), and tritide (3H-). The two stable 3H related defects under the Li2O-LiOH rich condition at T ¼ 1000 K, between P(O2) ¼ 10^-5 and 10^-25 atm were found to be 3H interstitial (3Hint) with charge of þ1 and neutral 3HLi (substitutional 3H on the Li site) in g-LiAlO2, Li2ZrO3, and Li2TiO3 materials. For Li2TiO3, the stability of the 3HO with charge of þ1 and 3HM with charge of 3 becomes comparable to that of 3Hint atm at P(O2) ¼ 1025 atm. Overall, our results indicate the energy cost to form the stable 3H defect species (3Hint with charge of þ1 and neutral 3HLi) in g-LiAlO2 material are higher than those of Li2TiO3 and Li2ZrO3 materials at T ¼ 1000 K, between P(O2) ¼ 10^-5 and 10^-25 atm. Interactions between the point defects and the 3H species as a function of the electron chemical potential across the band gap of the materials were also examined. All the three materials exhibit strong interactions between the 3Hint and the O vacancy, Li vacancy, and O interstitial defects with the strongest interactions found in the case of y-LiAlO2 phase, indicating a higher tendency to trap 3H at such point defects in y-LiAlO2.

Charged defect analysis, Tritium-producing burnabl↗
DOE OSTI2020

A Critical Review of High Burnup Fuel Fragmentation, Relocation, and Dispersal under Loss-Of-Coolant Accident Conditions

High burnup fuel in excess of ~6268 GWd/MTU has been observed to severely fragment when subjected to temperature transient conditions as in a loss-of-coolant accident. In the event of cladding failure, the severely fragmented fuel can relocate into the balloon region and potentially disperse through the burst opening into the reactor’s primary system. This observation has led to implementation of several independent test programs designed to focus on various aspects of fuel fragmentation, relocation, and dispersal. The purpose of this review is to perform a critical, holistic assessment of fuel fragmentation, relocation, and dispersal under loss-of-coolant conditions to identify data gaps in the experimental data base. Phenomena have been identified and well defined through the various testing programs, and general agreement regarding the governing parameters (temperature, burnup, heating rate, cladding deformation, etc.) has been established. However, there is a significant data gap connecting research to commercial application. Furthermore, previous research attempted to evaluate the problem holistically, but has yet to provide a comprehensive understanding of the problem. Major identified data gaps consist of (1) a comprehensive understanding of pretransient fuel rod conditions (fuel temperature, fuel stress, rod internal pressures, microstructure, etc.), (2) definition of prototypic loss-of-coolant accident conditions (heating rate, fuel temperatures, fuel stress), (3) identification of the differences between nuclear and electrical integral loss-of-coolant accident tests, and connection of fuel rod performance (cladding ballooning, burst opening, potential relocation, fragmentation susceptibility, etc.) to fuel fragmentation, relocation, and dispersal severity. Finally, the content of this manuscript provides a clear path for connecting testing and data acquisition to commercial application by providing concrete means for filling data gaps and prioritized testing regimes.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Modelica-based system modeling for studying control-related faults in chiller plants and boiler plants serving large office buildings

System modeling is critical when studying operation faults in chiller plants and boiler plants. However, current fault models have difficulties faithfully representing the operation of chiller plants and boiler plants under the effects of those faults, especially the control-related ones. In this study, we present a systematic method to develop high-fidelity models for approximating the behaviors of chiller plants and boiler plants under faulty conditions. Compared to existing ones, the resulting fault models have two advantages: first, they better characterize the dynamic patterns in the system operation. In those models, control architecture and control logic are faithfully implemented. Thus, they can be used to study control-related faults, such as incorrect staging control due to sensor bias and mistuned feedback control. Second, they are readily extensible and can support large-scale investigations to explore different faulty conditions/scenarios. Those models are established in a hierarchical structure while modules in each layer can be redeclared and parameterized at upper layers. In such a case, modifications to the models can be realized through model modifiers and the process can be easily streamlined with scripts. We applied the proposed models in a comprehensive fault impact evaluation of the 13 control-related faults of chiller and boiler plants. In this evaluation, the proposed plant model is coupled with the EnergyPlus thermal load model to study the impact of various faulty scenarios. Based on the evaluation results, we identified the faults that have the most significant impacts on the operation of the chiller and boiler plants, respectively. We also found that the relationship between the impacts of the studied faults and the severity level of the faults are highly non-linear

Huang, Sen↗
DOE OSTI2022

Model predictive control of heating, ventilation, and air conditioning (HVAC) systems: A state-of-the-art review

Due to the fast advancement of communication and information technology, intelligent buildings have garnered great interest. These buildings can forecast weather, ambient temperature, and sun irradiation and can modify heating, ventilation, and air conditioning (HVAC) operations appropriately, based on current and previous data. This change is intended to reduce HVAC system energy usage while maintaining an appropriate degree of thermal comfort and indoor air quality. Since its inception, model predictive control (MPC) has been one of the prospective solutions for HVAC management systems to reduce both costs and energy usage. Additionally, MPC is becoming increasingly practical as the processing capacity of building automation systems increases and a large quantity of monitored building data becomes available. MPC also provides the potential to improve the energy efficiency of HVAC systems via its capacity to consider limitations, to predict disruptions, and to factor in multiple competing goals such as interior thermal comfort and building energy consumption. Although substantial research has been conducted on MPC in building HVAC systems, there is a shortage of critical reviews and a lack of a comprehensive framework that formulates and defines the applications. Here, this article provides a comprehensive state-of-the-art overview of MPC in HVAC systems. Detailed discussions of modeling approaches and optimization algorithms are included. Numerous design aspects such as prediction horizon, occupancy behavior, building type, and cost function, that impact MPC performance are discussed in detail. The technical characteristics, advantages, and disadvantages of various types of modeling software are discussed. The primary objective of this work is to highlight critical design characteristics for the MPC control scheme and to give improved suggestions for future research. Moreover, numerous prospective scenarios have been suggested that might provide future research direction.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Revealing structural involvement of chromophores in algal light harvesting complexes using symmetry-adapted perturbation theory

The attribution of quantum beats observed in the time-resolved spectroscopy of photosynthetic light-harvesting antennae to nontrivial quantum coherences has sparked a flurry of research activity beginning a decade ago. Even though investigations into the functional aspects of photosynthetic light-harvesting were supported by X-ray crystal structures, the non-covalent interactions between pigments and their local protein environment that drive such function has yet to be comprehensively explored. Using symmetry-adapted perturbation theory (SAPT), we have comprehensively determined the magnitude and compositions of these non-covalent interactions involving light-harvesting chromophores in two quintessential photosynthetic pigment-protein complexes — peridinin chlorophyll-a protein (PCP) from dinoflagellate Amphidinium carterae and phycocyanin 645 (PC645) from cryptophyte Chroomonas mesostigmatica. In PCP, the chlorophylls are dispersion-bound to the peridinins, which in turn are electrostatically anchored to the protein scaffold via their polar terminal rings. This might be an evolutionary design principle in which the relative orientation of the carotenoids towards the aqueous environment determines the arrangement of the other chromophores in carotenoid-based antennas. On the other hand, electrostatics dominate the non-covalent interactions in PC645. As a result, our ab initio simulations also suggest full protonation of the PC645 chromophores in physiological conditions, and that changes to their protonation states result in their participation as switches between folded and unfolded conformations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Life-cycle implications of hydrogen fuel cell electric vehicle technology for medium- and heavy-duty trucks

This study provides a comprehensive and up-to-date life-cycle comparison of hydrogen fuel cell electric trucks (FCETs) and their conventional diesel counterparts in terms of energy use and air emissions, based on the ensemble of well-established methods, high-fidelity vehicle dynamic simulations, and real-world vehicle test data. For the centralized steam methane reforming (SMR) pathway, hydrogen FCETs reduce life-cycle or well-to-wheel (WTW) petroleum energy use by more than 98% compared to their diesel counterparts. The reduction in WTW air emissions for gaseous hydrogen (G.H2) FCETs ranges from 20-45% for greenhouse gases, 37-65% for VOC, 49-77% for CO, 62-83% for NOx, 19-43% for PM10, and 27-44% for PM2.5, depending on vehicle weight classes and truck types. With the current U.S. average electricity generation mix, FCETs tend to create more WTW SOx emissions than their diesel counterparts, mainly because of the upstream emissions related to electricity use for hydrogen compression/liquefaction. Compared to G.H2, liquid hydrogen (L.H2) FCETs generally provide smaller WTW emissions reductions. For both G.H2 and L.H2 pathways for FCETs, because of electricity consumption for compression and liquefaction, spatio-temporal variations of electricity generation can affect the WTW results. FCETs retain the WTW emission reduction benefits, even when considering aggressive diesel engine efficiency improvement.

air emissions↗