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DOE OSTI2016

On the evolution of high-redshift active galactic nuclei

We build a simple physical model to study the high-redshift active galactic nucleus (AGN) evolution within the co-evolution framework of central black holes (BHs) and their host galaxies. The correlation between the circular velocity of a dark halo V {sub c} and the velocity dispersion of a galaxy σ is used to link the dark matter halo mass and BH mass. The dark matter halo mass function is converted to the BH mass function for any given redshift. The high-redshift optical AGN luminosity functions (LFs) are constructed. At z∼4, the flattening feature is not shown at the faint end of the optical AGN LF. This is consistent with observational results. If the optical AGN LF at z∼6 can be reproduced in the case in which central BHs have the Eddington-limited accretion, it is possible for the AGN lifetime to have a small value of 2×10{sup 5} years. The X-ray AGN LFs and X-ray AGN number counts are also calculated at 2.0 3, respectively, using the same parameters adopted in the calculation for the optical AGN LF at z∼4. It is estimated that about 30 AGNs per deg{sup 2} at z>6 can be detected with a flux limit of 3×10{sup −17} erg cm{sup −2} s{sup −1} in the 0.5–2 keV band. Additionally, the cosmic reionization is also investigated. The ultraviolet photons emitted from the high-redshift AGNs mainly contribute to the cosmic reionization, and the central BHs of the high-redshift AGNs have a mass range of 10{sup 6}--10{sup 8}M{sub ⊙}. We also discuss some uncertainties in both the AGN LFs and AGN number counts originating from the M{sub BH}--σ relation, Eddington ratio, AGN lifetime, and X-ray attenuation in our model.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1999

Application of Single Crystal Failure Criteria: Theory and Turbine Blade Case Study

The orientation of the single crystal material within a structural component is known to affect the strength and life of the part. The first stage blade of the High Pressure Fuel Turbopump (HPFTP)/ Alternative Turbopump Development (ATD), of the Space Shuttle Main Engine (SSME) was used to study the effects of secondary axis'orientation angles on the failure rate of the blade. A new failure criterion was developed based on normal and shear strains on the primary crystallographic planes. The criterion was verified using low cycle fatigue (LCF) specimen data and a finite element model of the test specimens. The criterion was then used to study ATD/HPFTP first stage blade failure events. A detailed ANSYS finite element model of the blade was used to calculate the failure parameter for the different crystallographic orientations. A total of 297 cases were run to cover a wide range of acceptable orientations within the blade. Those orientations are related to the base crystallographic coordinate system that was created in the ANSYS finite element model. Contour plots of the criterion as a function of orientation for the blade tip and attachment were obtained. Results of the analysis revealed a 40% increase in the failure parameter due to changing of the primary and secondary axes of material orientations. A comparison between failure criterion predictions and actual engine test data was then conducted. The engine test data comes from two ATD/HPFTP builds (units F3- 4B and F6-5D), which were ground tested on the SSME at the Stennis Space Center in Mississippi. Both units experienced cracking of the airfoil tips in multiple blades, but only a few cracks grew all the way across the wall of the hollow core airfoil.

Sayyah, Tarek↗

NASA NTRS2005

Mass Spectrometry for Planetary Probes: Past, Present and Future

Atmospheric entry probes present a unique opportunity for performing quantitative analysis of extra-terrestrial atmospheres in cases where remote sensing alone may not be sufficient and measurements with balloons or aircraft is not practical. An entry probe can provide a complete vertical profile of atmospheric parameters including chemical composition, which cannot be obtained with most other techniques. There are, however, unique challenges associated with building instruments for an entry probe, as compared to orbiters, landers, or rovers. Conditions during atmospheric entry are extreme, there are inherent time constraints due to the short duration of the experiment, and the instrument experiences rapid environmental changes in temperature and pressure as it descends. In addition, there are resource limitations, i.e. mass, power, size and bandwidth. Finally, the demands on the instrument design are determined in large part by conditions (pressure, temperature, composition) unique to the particular body under study, and as a result there is no one-size-fits-all instrument for an atmospheric probe. Many of these requirements can be more easily met by miniaturizing the probe instrument. Our experience building mass spectrometers for atmospheric entry probes leads us to believe that the time is right for a fundamental change in the way spaceflight mass spectrometers are built. The emergence over the past twenty years of Micro-electro- mechanical Systems (MEMS), utilizing lithographic semiconductor fabrication techniques to produce instrument systems in miniature, holds great promise for application to spaceflight mass spectrometry. A highly miniaturized, high performance and low-power mass spectrometer would be an enormous benefit to future entry probe missions, allowing, for example, parallel measurements (e.g., multiple simultaneous gas chromatographic analyses and direct atmospheric leaks.) Such an instrument would also enable mass spectrometry on board small multiple entry probes. In the development of a MEMS Mass Spectrometer, the challenge facing us is to move beyond the proof-of-concept, where research dollars tend to focus, and carry out the detailed work of developing a high performance mass spectrometer system on a chip which meets the unique technical requirements for an atmospheric entry probe described above.

Niemann, Hasso B.↗

NASA NTRS2019

Experimental Demonstration and System Analysis for Plasmonic Force Propulsion

This project developed a higher-fidelity model of a recently envisioned small spacecraft propulsion system for precision pointing and proximity control. Plasmonic force propulsion harnesses solar light focused onto plasmon reactive subwavelength nanostructures to accelerate and expel nanoparticle propellant via strong optical forces. The goal of the project was to show that plasmonic space propulsion can provide the level of proximity and attitude control envisioned for future NASA nano/picosatellite missions, a level that is better than state-of-the-art approaches. We achieved this goal by showing that plasmonic force thrusters are feasible for a range of advanced mission concepts requiring swarm formations in a deep space environment. We performed three case studies that evaluated the performance of the plasmonic force propulsion thruster in a deep space, microsatellite swarm formation. These case studies assumed the propulsion system could generate thrust at the level predicted from our Phase 1 study (1.6 μN). Through these cases we were able to analyze the concept within a mission specific context through detailed orbital dynamics calculations. Results indicate that, with the Phase 1 estimated thrust level, the approach is promising for providing attitude control to swarm formation spacecraft. Further, we achieved goals related to technology development. Specifically, we experimentally demonstrated nanoparticle acceleration due to plasmonic forces with asymmetric nanostructures excited by focused laser light. Additionally, we investigated the thrust sensitivity and nanoparticle propellant injection dependencies upon thermal effects. As a result of our study, plasmonic force propulsion is at an early TRL 3. Active research and design has been conducted analytically and in the laboratory. Furthermore, practical applications such as the three case studies have been identified for the scientific basic principles that were observed. Future efforts related to fundamental understanding of these techniques should focus on 1) developing a standalone array of asymmetric nanostructures that can effectively interact with a stream or reservoir of particles or 2) experimentally evaluate a dielectrophoretic injector for nanoparticle propellant. The main limitation discovered about plasmonic propulsion regards performance estimates significantly below the Phase 1 estimations. Specifically, original assumptions in the Phase 1 project (notably, a linear array of asymmetric nanostructures) is not a viable approach to achieving significant acceleration, high exhaust velocity, of nanoparticles. More specifically, we assumed in Phase 1 that nanoparticles would be accelerated in series by a long linear array of asymmetric nanostructures. That is, the acceleration of the nanoparticle would build and increase with the kick received by each subsequent nanostructure. This is fundamentally flawed. The potential profile of a single nanostructure is such that it prohibits this phenomenon. The potential energy associated with the plasmon-generated dielectrophoretic force is a potential well, which is good for trapping nanoparticles, but cannot provide significant acceleration of particles to expel them out and away from the nanostructure. Further, a nanoparticle expelled from the first nanostructure would need to overcome the potential barrier for entry into the next nanostructure accelerating stage. Fundamentally, this effect means that a linear array of nanostructures is not a viable accelerating structure. Correspondingly then, acceleration can, or should, only be provided by one nanostructure, and the net acceleration and thrust force of a single nanostructure is small (~cm/s exhaust velocities, sub-nN level thrust vs. the 100’s m/s, μN originally envisioned). While our experiments demonstrated acceleration and manipulation of a nanoparticle using laser light in aqueous environment, the achievable energy and momentum addition to the nanoparticle from a single nanostructure stage is too low for useful propulsion. In terms of thrust prediction, the estimated thrust of 1.6 μN in Phase 1 is reduced to a few nN of thrust with this new insight and understanding of the concept. This thrust level is too small to achieve attitude control of swarms as originally envisioned.

Plasmonic Force↗

NASA NTRS2023

A computationally efficient statistically downscaled 100 m resolution Greenland product from the regional climate model MAR

The Greenland Ice Sheet (GrIS) has been contributing directly to sea level rise, and this contribution is projected to accelerate over the next decades. A crucial tool for studying the evolution of surface mass loss (e.g., surface mass balance, SMB) consists of regional climate models (RCMs), which can provide current estimates and future projections of sea level rise associated with such losses. However, one of the main limitations of RCMs is the relatively coarse horizontal spatial resolution at which outputs are currently generated. Here, we report results concerning the statistical downscaling of the SMB modeled by the Modèle Atmosphérique Régional (MAR) RCM from the original spatial resolution of 6 km to 100 m building on the relationship between elevation and mass losses in Greenland. To this goal, we developed a geospatial framework that allows the parallelization of the downscaling process, a crucial aspect to increase the computational efficiency of the algorithm. Using the results obtained in the case of the SMB, surface and air temperature are assessed through the comparison of the modeled outputs with in situ and satellite measurement. The downscaled products show a considerable improvement in the case of the downscaled product with respect to the original coarse output, with the coefficient of determination (R 2 ) increasing from 0.868 for the original MAR output to 0.935 for the SMB downscaled product. Moreover, the value of the slope and intercept of the linear regression fitting modeled and measured SMB values shifts from 0.865 for the original MAR to 1.015 for the downscaled product in the case of the slope and from the value −235 mm w.e. yr -1 (original) to −57 mm w.e. yr -1 (downscaled) in the case of the intercept, considerably improving upon results previously published in the literature.

Greenland Ice Sheet↗

DOE OSTI2021

Tri-Level Scheduling Model Considering Residential Demand Flexibility of Aggregated HVACs and EVs Under Distribution LMP

Residential loads, especially heating, ventilation and air conditioners (HVACs) and electric vehicles (EVs), have great potentials to provide demand flexibility which is an attribute of grid-interactive efficient buildings (GEB). Under this new paradigm, first, EV and HVAC aggregator models are developed in this paper to represent the fleet of GEBs, in which the aggregated parameters are obtained based on a new approach of data generation and least squares parameter estimation (DG-LSPE), which can deal with heterogeneous HVACs. Then, a tri-level bidding and dispatching framework is established based on competitive distribution operation with distribution locational marginal price (DLMP). Furthermore, the first two levels form a bilevel model to optimize the aggregators’ payment and to represent the interdependency between load aggregators and the distribution system operator (DSO) using DLMP, and the third level is to dispatch the optimal load aggregation to all residents by the proposed priority list-based demand dispatching algorithm. Finally, case studies on a modified IEEE 33-Bus system illustrate three main technical reasons of payment reduction due to demand flexibility: load shift, DLMP step changes, and power losses. They can be used as general guidelines for better decision-making for future planning and operation of demand response programs.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRSDate not supplied

Combining NASA Earth Observations and Commercial Smallsat Data to Inform Localized Decision Making

NASA's Earth Science Division's DEVELOP Program builds capacity in individuals and partner organizations to research the feasibility of using Earth observations for informed environmental decision making. Employing an internship-like model, DEVELOP conducts 10-week long feasibility studies that are focused on decision-making organizations' environmental concerns. These projects use the vantage point of space to address environmental issues across a broad set of themes, including agricultural monitoring, disaster risk and resilience planning, water resource and coastal management, wildfire cartography, health & air quality, and urban development. Following the establishment of NASA's Commercial Smallsat Data Acquisition (CSDA) Program, DEVELOP began adding commercial smallsat data into a subset of its feasibility projects. This presentation will highlight the program's use of CSDA data and its integration with NASA Earth observing fleet data, showcase example use cases, speak to challenges faced by the DEVELOP team in using CSDA data, and the broad array of thematic and topical applications created by DEVELOP teams.

Lisa Tanh↗

DOE OSTI2025

CLPNets: Coupled Lie–Poisson neural networks for multi-part Hamiltonian systems with symmetries

To accurately compute data-based prediction of Hamiltonian systems, it is essential to utilize methods that preserve the structure of the equations over time. We consider a particularly challenging case of systems with interacting parts that do not reduce to pure momentum evolution. Such systems are essential in scientific computations, such as discretization of a continuum elastic rod, which can be viewed as the group of rotations and translations $SE(3)$. The evolution involves not only the momenta but also the relative positions and orientations of the particles. The presence of Lie group-valued elements, such as relative positions and orientations, poses a problem for applying previously derived methods for data-based computing. We develop a novel method of data-based computation and complete phase space learning of such systems. We follow the original framework of SympNets (Jin et al., 2020) and LPNets (Eldred et al., 2024), building the neural network from phase space mappings that preserve the Lie–Poisson structure. We derive a novel system of mappings that are built into neural networks describing the evolution of such systems. We call such networks Coupled Lie–Poisson Neural Networks, or CLPNets. We consider increasingly complex examples for the applications of CLPNets, starting with the rotation of two rigid bodies about a common axis, progressing to the free rotation of two rigid bodies, and finally to the evolution of two connected and interacting $SE(3)$ components, describing the discretization of an elastic rod into two elements. Our method preserves all Casimir invariants to machine precision, preserves energy to high accuracy, and shows good resistance to the curse of dimensionality, requiring only a few thousand data points for all cases studied (three to eighteen dimensions). Additionally, the method is highly economical in memory requirements, requiring only about 200 parameters for the most complex case considered.

Data-based modeling↗

DOE OSTI2021

Improving ChemCam LIBS long-distance elemental compositions using empirical abundance trends

The ChemCam instrument on the Curiosity rover provides chemical compositions of Martian rocks and soils using remote laser-induced breakdown spectroscopy (LIBS). The elemental calibration is stable as a function of distance for Ti, Fe, Mg, and Ca. The calibration shows small, systematically increasing abundance trends as a function of distance for Al, Na, K, and to some extent, Si. The distance effect is known to be due to a dependence with distance on the relative strengths of atomic transition lines. Emission lines representing transitions from relatively low energy levels remain intense at longer distances while emission lines representing transitions from higher energy levels decrease in intensity more rapidly as a function of distance. The multivariate algorithms used to determine elemental compositions rely on a large number of emission lines in many cases, so rather than trying to correct all emission lines, a study was made of the predicted compositions as a function of distance, in order to determine an empirical correction. Abundance trends can be well approximated by a linear trend with distance within the ranges of abundances and distances observed up to ~6 m. Data from 11 distinct geological members and data groups of the Murray formation in Gale crater, Mars, were used to form the model, selecting the members and data groups yielding the best statistics. The model was tested using data from several targets observed from two different distances, and using data from the Kimberley formation, the composition of which is significantly different from the Murray formation, showing that the model works on other compositions beyond those used to build the model. For long-distance observations up to ~6 m, corrections can be made back to an equivalent composition at the median distance of ChemCam observations (2.6 m). Finally, the model has been validated up to 6.2 m, although ChemCam is able to observe bedrock targets to >7 m, and iron meteorites to distances of >9 m.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

KGML-ag: a modeling framework of knowledge-guided machine learning to simulate agroecosystems: a case study of estimating N<sub>2</sub>O emission using data from mesocosm experiments

Abstract. Agricultural nitrous oxide (N2O) emission accounts for a non-trivial fraction of global greenhouse gas (GHG) budget. To date, estimating N2O fluxes from cropland remains a challenging task because the related microbial processes (e.g., nitrification and denitrification) are controlled by complex interactions among climate, soil, plant and human activities. Existing approaches such as process-based (PB) models have well-known limitations due to insufficient representations of the processes or uncertainties of model parameters, and due to leverage recent advances in machine learning (ML) a new method is needed to unlock the “black box” to overcome its limitations such as low interpretability, out-of-sample failure and massive data demand. In this study, we developed a first-of-its-kind knowledge-guided machine learning model for agroecosystems (KGML-ag) by incorporating biogeophysical and chemical domain knowledge from an advanced PB model, ecosys, and tested it by comparing simulating daily N2O fluxes with real observed data from mesocosm experiments. The gated recurrent unit (GRU) was used as the basis to build the model structure. To optimize the model performance, we have investigated a range of ideas, including (1) using initial values of intermediate variables (IMVs) instead of time series as model input to reduce data demand; (2) building hierarchical structures to explicitly estimate IMVs for further N2O prediction; (3) using multi-task learning to balance the simultaneous training on multiple variables; and (4) pre-training with millions of synthetic data generated from ecosys and fine-tuning with mesocosm observations. Six other pure ML models were developed using the same mesocosm data to serve as the benchmark for the KGML-ag model. Results show that KGML-ag did an excellent job in reproducing the mesocosm N2O fluxes (overall r2=0.81, and RMSE=3.6 mgNm-2d-1 from cross validation). Importantly, KGML-ag always outperforms the PB model and ML models in predicting N2O fluxes, especially for complex temporal dynamics and emission peaks. Besides, KGML-ag goes beyond the pure ML models by providing more interpretable predictions as well as pinpointing desired new knowledge and data to further empower the current KGML-ag. We believe the KGML-ag development in this study will stimulate a new body of research on interpretable ML for biogeochemistry and other related geoscience processes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Integration of renewable energy generation and storage systems for emissions reduction in an islanded campus microgrid

Microgrid connected building communities are projected to play an integral part in the clean energy transition. These types of systems, when integrated with distributed energy resources (DERs) such as combined heat and power (CHP), district heating and cooling, renewable generation, and energy storage, can provide clean, reliable power to critical facilities and vulnerable communities. The intermittent nature of renewable generation is a challenge when integrating renewables into any grid system, but particularly in islanded microgrids. The University of Texas at Austin (UT) operates an islanded microgrid powered by a CHP plant, while also utilizing district heating and cooling systems and thermal energy storage (TES). High fidelity operating data was used to develop a validated reduced order model of UT’s integrated campus energy systems to serve as a testbed for use in a case study. Hypothetical renewable energy installations on land owned by UT in west Texas were modeled and integrated into the validated campus models along with battery energy storage (BES). Simulations showed that a combination of renewable energy from wind, and optimally controlled 24-hour thermal and battery storage systems could reduce carbon dioxide emissions on campus by 45.4%. The additional retrofit of burner systems to utilize hydrogen natural gas blends resulted in an overall annual emissions reduction of 54.7%. Carbon capture and storage eliminated the majority of the remaining emissions with increased plant energy expenditure. The presented simulations display the practical limitations of a CHP system complemented by renewable generation and short-term storage in eliminating emissions. Furthermore, results highlight the need for further research and development in long duration storage technologies and hydrogen fueled turbines to increase penetration of renewable energy and reduce emissions.

CHP↗

DOE OSTI2023

Graphene-based analog of single-slit electron diffraction

In this study, we report on the experimental demonstration of single-slit diffraction exhibited by electrons propagating in encapsulated graphene with an effective de Broglie wavelength corresponding to their attributes as massless Dirac fermions. Nanometer-scale device designs were implemented to fabricate a single-slit followed by five detector paths. Predictive calculations were also utilized to readily understand the observations reported. These calculations required the modeling of wave propagation in ideal case scenarios of the reported device designs to more accurately describe the observed single-slit phenomenon. This experiment was performed at room temperature and 190 K, where data from the latter highlighted the exaggerated asymmetry between electrons and holes, recently ascribed to slightly different Fermi velocities near the K point. This observation and device concept may be used for building diffraction switches with versatile applicability.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2026

Importance of finite-size corrections for accurate ab initio modeling of carrier capture at semiconductor defects: A case study of substitutional C N in GaN

In ab initio studies of carrier-capture processes in defective semiconductor materials, the single-effective-mode formalism and the static-coupling approximation have become the predominant theoretical approaches for determining carrier-capture coefficients. The single-mode formalism relies on accurate nonequilibrium defect energies obtained from density-functional theory (DFT), where required inputs are a series of configurationally displaced, defect-containing supercells obtained using an interpolative ansatz, and where the DFT outputs are corresponding total energies that have traditionally been postprocessed using a long-established ground-state formulation of finite-size corrections and defect-formation energies. This formulation remains commonly used even though the defects that form a configuration-coordinate (CC) diagram typically exist as structures that are displaced from the ground state. To remedy this inconsistency, Kumagai has recently proposed novel methods for implementing finite-size corrections specifically intended for DFT calculations of the defect energies used to construct CC diagrams and implement the single-mode formalism [Y. Kumagai, Phys. Rev. B 107, L220101 (2023)]. Kumagai's approach builds on the latest finite-size-correction methods introduced to describe vertical charge-state transitions for charge-localizing point defects in semiconductors and insulators [T. Gake et al., Phys. Rev. B 101, 020102 (2020); S. Falletta et al., Phys. Rev. B 102, 041115 (2020)]. The newly identified finite-size artifact treated in these studies is the polarization charge induced on a configurationally frozen defect and its subsequent interaction with a vertical transition in charge state. In this work, we evaluate Kumagai's proposed methodology by applying it in a high-precision DFT study of carrier capture by substitutional C N in GaN, a well-characterized and technologically relevant defect and material. We have rigorously calculated C N defect energies across various supercell sizes for each defect configuration and charge state on the hole-capture CC diagram of C N (𝑞=−1), enabling a direct comparison of the slopes of the defect energies versus inverse cell size with those predicted by Kumagai. The most consequential prediction of Kumagai's method is that these slopes distinctly vary as the square of the linear-interpolation parameter used to construct the nonequilibrium defect configurations. Our results quantitatively support this prediction. Moreover, with these new finite-size corrections and multiple-cell-size DFT calculations in place, we find that the classical energy barrier for hole capture by C N (𝑞=−1) in GaN decreases to 0.092–0.127 eV. This finding confirms the recent ≈ 0.1 eV prediction of Reshchikov based on the weak temperature dependence for hole capture observed in photoluminescence experiments [M. A. Reshchikov, J. Appl. Phys. 129, 121101 (2021)]. These results stand in stark contrast to previously calculated barriers of 0.486 and 0.73 eV, which also used the single-mode formalism but were obtained by instead using ground-state-based finite-size corrections. Our reduced classical barrier for capture increases the temperature-dependent hole-capture coefficient of a C N (𝑞=−1) defect by more than two to four orders of magnitude for temperatures of 100–600 K, compared to the previous 0.486 eV results. While other defects may not be as dramatically affected as here, we suggest that incorporating proper finite-size corrections for the vertical-transition-like states embedded within CC diagrams is an essential, yet previously unrecognized, component of accurate modeling of carrier-capture when using the single-effective-mode formalism.

dielectric properties↗

DOE OSTI2016

Study of a Docker Use-Case for HEP

Over the past few years, container technology has become increasingly promising as a means to seamlessly make our software available across a wider range of platforms. In December 2015, we decided to put together a set of docker images that serve as a demonstration of this container technology for managing a run-time environment for art-related software projects, and also serve as a set of test cases for evaluation of performance. Docker[1] containers provide a way to “wrap up a piece of software in a complete filesystem that contains everything it needs to run”. In combination with Shifter[2], such containers provide a way to run software developed and deployed on “typical” HEP platforms (such as SLF 6, in common use at Fermilab and on OSG platforms) on HPC facilities at NERSC. Docker containers provide a means of delivering software that can be run on a variety of hosts without needing to be compiled specially for each OS to be supported. This could substantially reduce the effort required to create and validate a new release, since one build could be suitable for use on both grid machines (both FermiGrid and OSG) as well as any machine capable of running the Docker container. In addition, docker containers may provide for a quick and easy way for users to install and use a software release in a standardized environment. This report contains the results and status of this demonstration and evaluation.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Three-dimensional alteration of neurites in schizophrenia

Psychiatric symptoms of schizophrenia suggest alteration of cerebral neurons. However, the physical basis of the schizophrenia symptoms has not been delineated at the cellular level. Here, we report nanometer-scale three-dimensional analysis of brain tissues of schizophrenia and control cases. Structures of cerebral tissues of the anterior cingulate cortex were visualized with synchrotron radiation nanotomography. Tissue constituents visualized in the three-dimensional images were traced to build Cartesian coordinate models of tissue constituents, such as neurons and blood vessels. The obtained Cartesian coordinates were used for calculating curvature and torsion of neurites in order to analyze their geometry. Results of the geometric analyses indicated that the curvature of neurites is significantly different between schizophrenia and control cases. The mean curvature of distal neurites of the schizophrenia cases was similar to 1.5 times higher than that of the controls. The schizophrenia case with the highest neurite curvature carried a frame shift mutation in the GLO1 gene, suggesting that oxidative stress due to the GLO1 mutation caused the structural alteration of the neurites. The differences in the neurite curvature result in differences in the spatial trajectory and hence alter neuronal circuits. It has been shown that the anterior cingulate cortex analyzed in this study has emotional and cognitive functions. We suggest that the structural alteration of neurons in the schizophrenia cases should reflect psychiatric symptoms of schizophrenia.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

Poster - 53: Improving inter-linac DMLC IMRT dose precision by fine tuning of MLC leaf calibration

Purpose: To develop a method to improve the inter-linac precision of DMLC IMRT dosimetry. Methods: The distance between opposing MLC leaf banks (“gap size”) can be finely tuned on Varian linacs. The dosimetric effect due to small deviations from the nominal gap size (“gap error”) was studied by introducing known errors for several DMLC sliding gap sizes, and for clinical plans based on the TG119 test cases. The plans were delivered on a single Varian linac and the relationship between gap error and the corresponding change in dose was measured. The plans were also delivered on eight Varian 2100 series linacs (at two institutions) in order to quantify the inter-linac variation in dose before and after fine tuning the MLC calibration. Results: The measured dose differences for each field agreed well with the predictions of LoSasso et al. Using the default MLC calibration, the variation in the physical MLC gap size was determined to be less than 0.4 mm between all linacs studied. The dose difference between the linacs with the largest and smallest physical gap was up to 5.4% (spinal cord region of the head and neck TG119 test case). This difference was reduced to 2.5% after fine tuning the MLC gap calibration. Conclusions: The inter-linac dose precision for DMLC IMRT on Varian linacs can be improved using a simple modification of the MLC calibration procedure that involves fine adjustment of the nominal gap size.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2013

Orthogonal decomposition as a design tool: With application to a mixing impeller

Digital manufacturing eliminates the expense and time required to develop custom products. By utilizing this technology, designers can quickly create a customized product specifically for their performance needs. But the timescale and expense from the engineering design workflows used to develop these customized products have not been adapted from the workflows used in mass production. In many cases these customized designs build upon already successful mass-produced products that were developed using conventional engineering design workflows. Many times as part of this conventional design process significant time is spent creating and validating high fidelity models that accurately predict the performance of the final design. These existing validated high fidelity models used for the mass-produced design can be reused for analysis and design of unknown products. This thesis explores the integration of reduced order modeling and detailed analysis into the engineering design workflow developing a customized design using digital manufacturing. Specifically, detailed analysis is coupled with proper orthogonal decomposition to enable the exploration of the design space while simultaneously shaping the model representing the design. This revised workflow is examined using the design of a laboratory scale overhead mixer impeller. The case study presented here is compared with the design of the Kar Dynamic Mixer impeller developed by The Dow Chemical Company. The result of which is a customized design for a refined set of operating conditions with improved performance.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2021

An Economics-by-Design Approach Applied to a Heat Pipe Microreactor Concept

Microreactors present a potential paradigm shift in the nuclear industry. Emphasis thus far has been on large-scale multi-billion-dollar projects that cater solely to grid electricity market. These projects can be challenging to finance and execute. On the other hand, microreactors are intended to target a wide variety of smaller niche markets and are expected to be factory-fabricated and more readily deployable. While diseconomies of scale for microreactors may tend to raise their costs per energy output (MWh) relative to large nuclear plants, offsetting gains can be expected from standardization, simplification, passive safety, lower radionuclide inventories, factory fabrication, fast installation, and low financing costs. To adequately assess these contributions, designers should have a different perspective on cost drivers than for large nuclear plants and can utilize novel approaches for systematic cost reduction. To account for these important aspects of microreactors, this report proposes an economics-by-design approach that places economic considerations at the center of the design process. The methodology builds on existing frameworks such as design-to-cost and value engineering, expanding them to new markets (beyond the grid), new attributes (beyond costs alone), and introducing the approach at earlier points in the design cycle. Design parameters and technical specifications are systematically evaluated until costs meet market entry points, while also providing the high-priority performance attributes of the particular use case. Determining first-order estimates for different components early in the process enables designers to focus R&D efforts on the biggest overall cost contributors and components with the most cost uncertainty. The analysis is always guided by market needs and threshold prices. In addition to microreactors, the approach is expected to be useful for other classes of nuclear reactors as well. The analysis was applied to a concept found in the open literature (the Design A heat-pipe reactor). A comprehensive bottom-up estimate was generated by leveraging a new microreactor-specific code of accounts and a range of cost equations. The initial estimate for levelized cost of electricity (LCOE) unsurprisingly exceeded market ranges since the use case had prioritized technological readiness over economic considerations in design choices. An alternate concept was then proposed, with various assumptions/targets made to reduce the largest cost contributors. Changes in the neutron spectrum, the power output, and building structures were found to make even the first-of-a-kind of this modified concept competitive with diesel generation in some remote communities. Learning rate (LR) assumptions indicated cost reductions achieved from sequential unit deployments could expand the range of competitiveness to include additional markets as deployments proceed.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

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