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At least 631 records · Page 35

Double-Shell Tank Primary Bottom Visual Inspection Results - 20025

A pressing need for the Double-Shell Tank Integrity Program at Hanford is of the ability to inspect the bottom of the tanks. Though the DSTs' annulus spaces (the space between the internal and external tank walls) have been inspected visually since 1992 and with Ultrasonic Testing (UT) since 1996, inspections of the tank bottoms have been able to be conducted due to limitations of available technology, omitting about 40 percent of the tank surface below the maximum fill level from routine inspection. Visual inspections of the tank annulus have been conducted up to five times in some DSTs and portions of the annulus space for all of the DSTs has been inspected twice with UT. This region includes the walls of the primary tank and secondary liner along with accessible portions of the secondary liner bottom. Due to this lack of technology, the degradation of the primary tank bottom in Tank AY-102 was undetected until the tank leaked into its annulus space, confirmed in 2012. Since that time, WRPS has been working with the nondestructive examination community and the Pacific Northwest National Laboratory, to evaluate potential technologies to conduct these challenging remote inspections. Recent work has included the development, testing, and deployment of several remote visual inspection technologies to observe the condition of the primary tank bottom of the DSTs by leveraging air distribution slots in the refractory pad on which it rests. Robotic solutions to conduct this inspection were developed with a detailed understanding of the physical configuration and operational logistics. Understanding and subsequent communication of these details to vendors within the robotics and nondestructive examination community was a critical component to the successful production of these new tools. Throughout the FY16-FY18 years, vendors from nondestructive examination community were solicited for ideas to meet the tank bottom inspection challenge. Two tank bottom visual inspection solutions were then pursued to meet that challenge. Both tools were successfully deployed at Tank AP-107, and provided valuable visuals, never before obtained from the under tank environment of a double-shell tank. While there are minor opportunities for improvement identified with each system, general outcomes were overwhelmingly positive. Both systems were successfully able to navigate the refractory air slot pattern from the outer edge to the tank center and record video footage. The micro-crawler provided high quality and very stable footage through the air slot pattern, but took longer to deploy into the slots and traverse through them than the alternative system. The push-pull tethered camera was able to navigate all seven of its target air slots in a matter of a few hours, which included deployment, movement between slot entrances, and complete removal of the system from the annulus environment. Through these air slots, new information was gained about the tank bottom and refractory pad condition of tank AP-107, AP-106, AP-108, and AN-102. While some debris was noted in the air slots, including sand, chipped refractory, and construction materials, the refractory was found to be structurally stable and adequately supporting the primary tank bottom through the full diameter of the tank. Primary tank bottom steel exterior was found to be in good condition, free of ongoing or aggressive corrosion conditions. Looking toward the future, robotic access to and visual inspection of refractory air slots and the primary tank bottom steel has now proven viable. Incorporating periodic air slot inspections as a regular practice within the Double-Shell Tank Integrity Program is the next step, allowing trending of any changes in condition. As ultrasonic testing infrastructure was leveraged to complete the initial system deployments described in this report, it makes sense to adopt that same strategy for ongoing periodic examinations and deploy these primary tank bottom inspection tools alongside ultrasonic testing operations. Development of visual inspection tools to evaluate the primary tank bottom region of the double-shell tanks was planned as the first stage of inspection. Continued development of volumetric sensors and incorporation of these inspection tools into Hanford's ongoing Double-Shell Tank Integrity Program are the next steps, seeking to continuously expand understanding of asset integrity and remaining useful life. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Information Content of a Synergy of Ground-Based and Space-Based Infrared Sounders. Part I: Clear-Sky Environments

Abstract Profiles of atmospheric temperature and water vapor from remotely sensed platforms provide critical observations within the temporal and spatial gaps of the radiosonde network. The 2017 National Academies of Science Decadal Survey highlighted that observations of the planetary boundary layer (PBL) from the current space-based observing system are not of the necessary accuracy or resolution for monitoring and predicting high-impact weather phenomena. One possible solution to improving observations of the PBL is supplementing the existing space-based observing system with a network of ground-based profilers. A synthetic information content study is developed utilizing profiles from the Atmospheric Radiation Measurement (ARM) program sites at the Southern Great Plains (SGP), east North Atlantic (ENA), and North Slope of Alaska (NSA) to assess the benefits, in terms of degrees of freedom (DOF), vertical resolution, and uncertainties, of a synergy between the ground-based Atmospheric Emitted Radiance Interferometer (AERI) with space-based hyperspectral infrared (IR) sounders. A combination of AERI with any of the three polar-orbiting IR sounders: the Atmospheric Infrared Sounder (AIRS), the Cross-track Infrared Sounder (CrIS), or the Infrared Atmospheric Sounding Interferometer (IASI), results in a DOF increase of 30%–40% in the surface-to-700-hPa layer compared to the space-based instrument alone. Introducing AERI measurements to the observing system also results in significant improvements to vertical resolution and uncertainties in the bottom 1000 m of the atmosphere compared to CrIS measurements alone. A synergy of CrIS and AERI exceeds the 1-km-vertical-resolution goal set by the Decadal Survey in the lowest 1000 m.

54 ENVIRONMENTAL SCIENCES↗

The geometry of the modular bootstrap

Abstract We explore the geometry behind the modular bootstrap and its image in the space of Taylor coefficients of the torus partition function. In the first part, we identify the geometry as an intersection of planes with the convex hull of moment curves onR + ⊗ℤ, with boundaries characterized by the total positivity of generalized Hankel matrices. We phrase the Hankel constraints as a semi-definite program, which has several advantages, such as the validity of bounds irrespective of spin truncation. We derive bounds on the gap, twist-gap, and the space of Taylor coefficients themselves. We find that if the gap is above$$ {\Delta }_{\textrm{gap}}^{\ast } $$ ∆ gap ∗ , where$$ \frac{c-1}{12}<{\Delta}_{\textrm{gap}}^{\ast }<\frac{c}{12} $$ c − 1 12 < Δ gap ∗ < c 12 , all coefficients become bounded on both sides and kinks develop in the space. In the second part, we propose an analytic method of imposing the integrality condition for the degeneracy number in the spinless bootstrap, which leads to a non-convex geometry. We find that even at very low derivative order this condition rules out regions otherwise allowed by bootstraps at high derivative order.

Physics↗

EFFECTS OF DIFFUSE BACKGROUND EMISSION AND SOURCE CROWDING ON PHOTOMETRIC COMPLETENESS IN SPITZER SPACE TELESCOPE IRAC SURVEYS: THE GLIMPSE CATALOGS AND ARCHIVES

We characterize the completeness of point source lists from Spitzer Space Telescope surveys in the four Infrared Array Camera (IRAC) bandpasses, emphasizing the Galactic Legacy Infrared Mid-Plane Survey Extraordinaire (GLIMPSE) programs (GLIMPSE I, II, 3D, 360; Deep GLIMPSE) and their resulting point source Catalogs and Archives. The analysis separately addresses effects of incompleteness resulting from high diffuse background emission and incompleteness resulting from point source confusion (i.e., crowding). An artificial star addition and extraction analysis demonstrates that completeness is strongly dependent on local background brightness and structure, with high-surface-brightness regions suffering up to five magnitudes of reduced sensitivity to point sources. This effect is most pronounced at the IRAC 5.8 and 8.0 {mu}m bands where UV-excited polycyclic aromatic hydrocarbon emission produces bright, complex structures (photodissociation regions). With regard to diffuse background effects, we provide the completeness as a function of stellar magnitude and diffuse background level in graphical and tabular formats. These data are suitable for estimating completeness in the low-source-density limit in any of the four IRAC bands in GLIMPSE Catalogs and Archives and some other Spitzer IRAC programs that employ similar observational strategies and are processed by the GLIMPSE pipeline. By performing the same analysis on smoothed images we show that the point source incompleteness is primarily a consequence of structure in the diffuse background emission rather than photon noise. With regard to source confusion in the high-source-density regions of the Galactic Plane, we provide figures illustrating the 90% completeness levels as a function of point source density at each band. We caution that completeness of the GLIMPSE 360/Deep GLIMPSE Catalogs is suppressed relative to the corresponding Archives as a consequence of rejecting stars that lie in the point-spread function wings of saturated sources. This effect is minor in regions of low saturated star density, such as toward the Outer Galaxy; this effect is significant along sightlines having a high density of saturated sources, especially for Deep GLIMPSE and other programs observing closer to the Galactic center using 12 s or longer exposure times.

79 ASTRONOMY AND ASTROPHYSICS↗

Small Angle Neutron Scattering Studies of R67 Dihydrofolate Reductase, a Tetrameric Protein with Intrinsically Disordered N-Termini

R67 dihydrofolate reductase (DHFR) is a homotetramer with a single active site pore and no sequence or structural homology with chromosomal DHFRs. The R67 enzyme provides resistance to trimethoprim, an active site-directed inhibitor of Escherichia coli DHFR. Sixteen to twenty N-terminal amino acids are intrinsically disordered in the R67 dimer crystal structure. Chymotrypsin cleavage of 16 N-terminal residues results in an active enzyme with a decreased stability. We investigated the space sampled by the disordered N-termini of R67 DHFR using small angle neutron scattering. From a combined analysis using molecular dynamics and the program SASSIE (http://www.smallangles.net/sassie/SASSIE_HOME.html), the apoenzyme displays a radius of gyration (Rg) of 21.46 ± 0.50 Å. Addition of glycine betaine, an osmolyte, does not result in folding of the termini as the Rg increases slightly to 22.78 ± 0.87 Å. SASSIE fits of the latter SANS data indicate that the disordered N-termini sample larger regions of space and remain disordered, suggesting they might function as entropic bristles. Pressure perturbation calorimetry also indicated that the volume of R67 DHFR increases upon addition of 10% betaine and decreased at 20% betaine because of the dehydration of the protein. Studies of the hydration of full-length R67 DHFR in the presence of the osmolytes betaine and dimethyl sulfoxide find around 1250 water molecules hydrating the protein. Similar studies with truncated R67 DHFR yield around 400 water molecules hydrating the protein in the presence of betaine. The difference of ~900 waters indicates the N-termini are well-hydrated.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Brickworx builds recurrent RNA and DNA structural motifs into medium- and low-resolution electron-density maps

A computer program that builds crystal structure models of nucleic acid molecules is presented. Brickworx is a computer program that builds crystal structure models of nucleic acid molecules using recurrent motifs including double-stranded helices. In a first step, the program searches for electron-density peaks that may correspond to phosphate groups; it may also take into account phosphate-group positions provided by the user. Subsequently, comparing the three-dimensional patterns of the P atoms with a database of nucleic acid fragments, it finds the matching positions of the double-stranded helical motifs (A-RNA or B-DNA) in the unit cell. If the target structure is RNA, the helical fragments are further extended with recurrent RNA motifs from a fragment library that contains single-stranded segments. Finally, the matched motifs are merged and refined in real space to find the most likely conformations, including a fit of the sequence to the electron-density map. The Brickworx program is available for download and as a web server at http://iimcb.genesilico.pl/brickworx.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

rmc-discord : reverse Monte Carlo refinement of diffuse scattering and correlated disorder from single crystals

A user-friendly program has been developed to analyze diffuse scattering from single crystals with the reverse Monte Carlo method. The approach allows for refinement of correlated disorder from atomistic supercells with magnetic or structural (occupational and/or displacive) disorder. The program is written in Python and optimized for performance and efficiency. Refinements of two user cases obtained with legacy neutron-scattering data demonstrate the effectiveness of the approach and the developed program. It is shown with bixbyite, a naturally occurring magnetic mineral, that the calculated three-dimensional spin-pair correlations are resolved with finer real-space resolution compared with the pair distribution function calculated directly from the reciprocal-space pattern. With the triangular lattice Ba 3 Co 2 O 6 (CO 3 ) 0.7 , refinements of occupational and displacive disorder are combined to extract the one-dimensional intra-chain correlations of carbonate molecules that move toward neighboring vacant sites to accommodate strain induced by electrostatic interactions. The program is packaged with a graphical user interface and extensible to serve the needs of single-crystal diffractometer instruments that collect diffuse-scattering data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

RAVEN User Manual

RAVEN is a generic software framework to perform parametric and probabilistic analysis based on the response of complex system codes. The initial development was aimed to provide dynamic risk analysis capabilities to the Thermo-Hydraulic code RELAP-7, currently under development at the Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose probabilistic and uncertainty quantification platform, capable to agnostically communicate with any system code. This agnosticism includes providing Application Programming Interfaces (APIs). These APIs are used to allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by inputs files or via python interfaces. RAVEN is capable of investigating the system response, and investigating the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused toward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. The development of RAVEN has started in 2012, when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework became stronger. RAVEN principal assignment is to provide the necessary software and algorithms in order to employ the concept developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just the individuation of the frequency of an event potentially leading to a system failure, but the closeness (or not) to key safety-related events. Hence, the approach is interested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. for an important process such as peak pressure in a pipe) is exceeded under certain conditions. The initial development of RAVEN has been focused on providing dynamic risk assessment capability to RELAP-7, currently under development at the INL and, likely, future replacement of the RELAP5-3D code. Most the capabilities that have been implemented having RELAP-7 as principal focus are easily deployable for other system codes. For this reason, several side activaties are currently ongoing for coupling RAVEN with software such as RELAP5-3D, etc. The aim of this document is the explanation of the input requirements, focalizing on the input structure.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

RAVEN User Manual

RAVEN is a generic software framework to perform parametric and probabilistic analy- sis based on the response of complex system codes. The initial development was aimed to provide dynamic risk analysis capabilities to the Thermo-Hydraulic code RELAP-7, currently under development at the Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose probabilistic and uncer- tainty quantification platform, capable to agnostically communicate with any system code. This agnosticism includes providing Application Programming Interfaces (APIs). These APIs are used to allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by inputs files or via python interfaces. RAVEN is capable of investigating the system response, and investigating the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused to- ward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. The development of RAVEN has started in 2012, when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework became stronger. RAVEN principal assignment is to provide the necessary software and algorithms in order to employ the concept developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just the individuation of the frequency of an event potentially leading to a system failure, but the closeness (or not) to key safety-related events. Hence, the approach is in- terested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. for an important process such as peak pressure in a pipe) is exceeded under certain conditions. The initial development of RAVEN has been focused on providing dynamic risk assess- ment capability to RELAP-7, currently under develop-ment at the INL and, likely, future replacement of the RELAP5-3D code. Most the capabilities that have been implemented having RELAP-7 as principal focus are easily deployable for other system codes. For this reason, several side activates are currently ongoing for coupling RAVEN with soft- ware such as RELAP5-3D, etc. The aim of this document is the explaination of the input requirements, focalizing on the input structure.

97 MATHEMATICS AND COMPUTING↗

RAVEN User Manual

RAVEN is a generic software framework to perform parametric and probabilistic analy- sis based on the response of complex system codes. The initial development was aimed to provide dynamic risk analysis capabilities to the Thermo-Hydraulic code RELAP-7, currently under development at the Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose probabilistic and uncer- tainty quantification platform, capable to agnostically communicate with any system code. This agnosticism includes providing Application Programming Interfaces (APIs). These APIs are used to allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by inputs files or via python interfaces. RAVEN is capable of investigating the system response, and investigating the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused to- ward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. The development of RAVEN has started in 2012, when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework became stronger. RAVEN principal assignment is to provide the necessary software and algorithms in order to employ the concept developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just the individuation of the frequency of an event potentially leading to a system failure, but the closeness (or not) to key safety-related events. Hence, the approach is in- terested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. for an important process such as peak pressure in a pipe) is exceeded under certain conditions. The initial development of RAVEN has been focused on providing dynamic risk assess- ment capability to RELAP-7, currently under develop-ment at the INL and, likely, future replacement of the RELAP5-3D code. Most the capabilities that have been implemented having RELAP-7 as principal focus are easily deployable for other system codes. For this reason, several side activates are currently ongoing for coupling RAVEN with soft- ware such as RELAP5-3D, etc. The aim of this document is the explaination of the input requirements, focalizing on the input structure.

97 MATHEMATICS AND COMPUTING↗

RAVEN User Manual, Revision 4

RAVEN is a generic software framework to perform parametric and probabilistic analysis based on the response of complex system codes. The initial development was aimed to provide dynamic risk analysis capabilities to the Thermo-Hydraulic code RELAP-7, currently under development at the Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose probabilistic and uncertainty quantification platform, capable to agnostically communicate with any system code. This agnosticism includes providing Application Programming Interfaces (APIs). These APIs are used to allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by inputs files or via python interfaces. RAVEN is capable of investigating the system response, and investigating the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused to- ward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. The development of RAVEN has started in 2012, when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework became stronger. RAVEN principal assignment is to provide the necessary software and algorithms in order to employ the concept developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just the individuation of the frequency of an event potentially leading to a system failure, but the closeness (or not) to key safety-related events. Hence, the approach is interested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. for an important process such as peak pressure in a pipe) is exceeded under certain conditions. The initial development of RAVEN has been focused on providing dynamic risk assess- ment capability to RELAP-7, currently under development at the INL and, likely, future replacement of the RELAP5-3D code. Most the capabilities that have been implemented having RELAP-7 as principal focus are easily deployable for other system codes. For this reason, several side activates are currently ongoing for coupling RAVEN with soft- ware such as RELAP5-3D, etc. The aim of this document is the explanation of the input requirements, focalizing on the input structure.

97 MATHEMATICS AND COMPUTING↗

RAVEN User Manual

RAVEN is a generic software framework to perform parametric and probabilistic analysis based on the response of complex system codes. The initial development was aimed to provide dynamic risk analysis capabilities to the Thermo-Hydraulic code RELAP-7, currently under development at the Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose probabilistic and uncertainty quantification platform, capable to agnostically communicate with any system code. This agnosticism includes providing Application Programming Interfaces (APIs). These APIs are used to allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by inputs files or via python interfaces. RAVEN is capable of investigating the system response, and investigating the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused to- ward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. The development of RAVEN has started in 2012, when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework became stronger. RAVEN principal assignment is to provide the necessary software and algorithms in order to employ the concept developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just the individuation of the frequency of an event potentially leading to a system failure, but the closeness (or not) to key safety-related events. Hence, the approach is interested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. for an important process such as peak pressure in a pipe) is exceeded under certain conditions. The initial development of RAVEN has been focused on providing dynamic risk assessment capability to RELAP-7, currently under development at the INL and, likely, future replacement of the RELAP5-3D code. Most the capabilities that have been implemented having RELAP-7 as principal focus are easily deployable for other system codes. For this reason, several side activates are currently ongoing for coupling RAVEN with soft- ware such as RELAP5-3D, etc. The aim of this document is the explanation of the input requirements, focalizing on the input structure.

97 MATHEMATICS AND COMPUTING↗

Critical Configuration and Physics Measurements for Beryllium Reflected Assemblies of U(93.15)O 2 Fuel Rods (1.506-cm Pitch)

Cadmium ratios were measured with enriched uranium metal foils at various locations in the assembly with the fuel tube at the 1.506-cm spacing. They are described in the following subsections. The experiment configuration was the same as the first critical configuration described in HEU-COMP-FAST-004 (Case 1). The experimenter placed 0.75-cm-diameter × 0.010-cm-thick 93.15%-235U-enriched uranium metal foils with and without 0.051-cm-thick cadmium covers at various locations in the core and top reflector. One part of the cadmium cover was cupshape and contained the uranium foil. The other part was a lid that fit over the exposed side of the foil when it was in the cup shaped section of the cover. As can be seen in the logbook, two runs were required to obtain all the measurements necessary for the cadmium ratio. The bare foil measurements within the top reflector were run first as part of the axial foil activation measurements. The results of this run are used for both the axial activation results and the cadmium ratios. Cadmium covered foils were then placed at the same location through the top reflector in a different run. Three pairs of bare and cadmium covered foils were also placed through the core tank. One pair was placed at the axial center of a fuel tube 11.35 cm from the center of the core. Two pairs of foils were placed on top of fuel tubes 3.02 and 12.06 cm from the center of the core. The activation of the uranium metal foils was measured after removal from the assembly using two lead shielded NaI scintillation detectors as follows. The NaI scintillators were carefully matched and had detection efficiencies for counting delayed-fission-product gamma rays with energies above 250 KeV within 5%. In all foil activation measurements, one foil at a specific location was used as a normalizing foil to remove the effects of the decay of fission products during the counting measurements with the NaI detectors. The normalization foil was placed on one NaI scintillator and the other foil on the other NaI detector and the activities measured simultaneously. The activation of a particular foil was compared to that of the normalization foil by dividing the count rate for each foil by that of the normalization foil. To correct for the differing efficiencies of the two NaI detectors, the normalization foil was counted in Detector 1 simultaneously with the foil at position x in Detector 2, and then the normalization foil was counted simultaneously in Detector 2 with the foil from position x in Counter 1. The activity of the foil from position x was divided by the activity of the normalization foil counted simultaneously. This resulted in obtaining two values of the ratio that were then averaged. This procedure essentially removed the effect of the differing efficiencies of the two NaI detectors. Differing efficiencies of 10% resulted in errors in the ratios measured to less than 1%. The background counting rates obatined with the foils used for the measurements on the NaI detectors before their irradiation measurement were subtracted from all count rates. The results of the cadmium ratio measurements are given in Table 1.3-1 and Figure 1.3-1. “No correction has been made for self shielding in the foils” (Reference 3).

1.506-cm pitch↗

Better Buildings Alliance: 2013 Annual Report

We are pleased to share with you a copy of the 2013 Annual Report. Inside, you’ll find significant program accomplishments, profiles on highlighted members, and plans for 2014. With your contributions, support, and leadership over the past 12 months, the program has reached significant milestones, including: Growing membership to over 200 members, to represent over 10 billion square feet of U.S. commercial building space and one-seventh of the market; Increasing participation in the 15 Solutions Teams by 75%; Developing 3 new high-efficiency technology specifications that if widely implemented, could save more than $5 billion in energy costs per year; Launching the Advanced RTU Campaign and Wireless Meter Challenge, and surpassing 100 million sq. ft. in the Lighting Energy Efficiency in Parking (LEEP) Campaign; Welcoming partners in new sectors, including K-12 schools and local governments; The program is a critical element of the Better Buildings Initiative, driving 20% energy savings in the building sector by 2020 through innovation, new technologies, and profiling leadership. Thank you for your ongoing participation, we are looking forward to working with you in the new year on your energy saving targets and advancing technical and market practices that promote energy savings at your organization.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Nonadiabatic Molecular Dynamics by Multiconfiguration Pair-Density Functional Theory

Herein, we present the first implementation of multiconfiguration pair-density functional theory (MC-PDFT) ab initio molecular dynamics. MC-PDFT is a multireference electronic structure method that in many cases has a similar accuracy (or even better accuracy) the complete active space second-order perturbation theory (CASPT2) at a significantly lower computational cost. In this study, we introduced MC-PDFT analytical gradients into the SHARC molecular dynamics program for ab initio, nonadiabatic molecular dynamics simulations. We verify our implementation by examining the intersystem crossing dynamics of thioformaldehyde, and we observe excellent agreement with recent CASPT2 and experimental findings. Moreover, with MC-PDFT, we could perform dynamics simulations with the 12 electron in 10 orbitals active space that was computationally too expensive for direct dynamics with CASPT2.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Infrared intrusion detection system (IRIDS)

A system and method for intrusion detection includes an imager directed towards an object in an interior space. The imager is in data communication with a computer. The computer is arranged to process digital three-dimensional image data received from the imager and programmed to execute a change detection algorithm in response to the processed three-dimensional data to determine movement of the object. The computer generates an alarm output in response to detecting movement of the object above a predetermined threshold. The method includes providing an imager directed towards an object in an interior space; receiving Time of Flight signals by the imager; processing digital three-dimensional image data received from the imager; and executing a change detection algorithm in response to the processed three-dimensional data to determine movement of the object.

Russell, John L.↗

High Resolution Data Analysis: Plans and Prospects [Book Chapter]

Herein, a report on the progress on the high resolution data analysis of the ADMX experimental results is presented. In this paper, tools are developed and tested on a blind injection mimicking a Maxwellian like signal in the frequency domain. This blind injection will be used as a test bed which can be later implemented on all the high resolution data. The high resolution data is stored in the Fermilab server. In this analysis a PostgreSQL query was made to ensure the blind injection is in the middle of the frequency spectrum and 19 such files were found. The time series data is read using a c++ program. An apodization function is applied on the time series data and zero filled to reduce the frequency spacing in order to achieve a better interpolation. A FFTW header is used to compute the Fourier transform of the time series data. A Savitzky–Golay filter is applied on the unnormalized power which then can be used to remove the spectral shape. Each frequency spectrum has a bandwidth of 50 kHz.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Polyhedral approximation in mixed-integer convex optimization

Generalizing both mixed-integer linear optimization and convex optimization, mixed-integer convex optimization possesses broad modeling power but has seen relatively few advances in general-purpose solvers in recent years. Here, we intend to provide a broadly accessible introduction to our recent work in developing algorithms and software for this problem class. Our approach is based on constructing polyhedral outer approximations of the convex constraints, resulting in a global solution by solving a finite number of mixed-integer linear and continuous convex subproblems. The key advance we present is to strengthen the polyhedral approximations by constructing them in a higher-dimensional space. In order to automate this extended formulation we rely on the algebraic modeling technique of disciplined convex programming (DCP), and for generality and ease of implementation we use conic representations of the convex constraints. Although our framework requires a manual translation of existing models into DCP form, after performing this transformation on the MINLPLIB2 benchmark library we were able to solve a number of unsolved instances and on many other instances achieve superior performance compared with state-of-the-art solvers like Bonmin, SCIP, and Artelys Knitro.

97 MATHEMATICS AND COMPUTING↗