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At least 703 records · Page 39

Chance-constrained Service Restoration for Distribution Networks with Renewables

Power systems operation is facing great challenges from natural disasters and cyber-attacks. It is critical but also difficult to enhance the reliability and resilience against extreme events. To better response to inevitable outages or blackouts, service restoration in distribution networks is important to minimize the disastrous impacts of catastrophic events. The increasing penetration of distributed energy resources (DERs) provides new opportunities to expedite the restoration process. However, the coordination with conventional distribution system control devices and the uncertainty and variability of intermittent renewable energy resources requires new operation and control strategies for distribution service restoration (DSR). This paper develops an optimal bottom-up DSR strategy by coordinating DERs with voltage regulators and capacitor banks. The chance-constrained (CC) programming approach is used to model the probabilistic output limit of solar radiation and PV generation. The Markov's inequality and Latin hypercube sampling techniques are applied to convert and incorporate the chance constraints into the DSR optimization problem. The CC-DSR problem is formulated as a mixed integer convex programming problem, considering various operational cost functions and bidirectional three-phase unbalanced load flow. Simulation results on the modified IEEE 13-node test feeder system demonstrate the effectiveness and flexibility of the bottom-up DSR strategy.

Chance constrained optimization, Distribution serv↗

Accounting for hydrological and microbial processes on greenhouse gas budgets from river systems (Final Report)

River systems have a disproportionately large impact on carbon and nitrogen cycling on this planet. For instance, despite their much smaller size in area, it is estimated that global carbon dioxide emission from river systems exceed the emission from lakes and reservoirs, and are on par with the estimated oceanic and land carbon sinks. Closing carbon and nitrogen budgets in river systems requires knowledge of end member greenhouse gas (GHG) products (e.g. carbon dioxide, nitrous oxide, methane), which typically have not been measured in river systems. Moreover, while the production of these gasses is thought to be microbially derived, little is known about microbial activities in sediments of large rivers, despite their potentially high influence on biogeochemical budgets. Typically, when we think of rivers we visualize the channels that carve through the earth, but this only represents a small part of the hydrologic exchange that a river participates in. Rivers are more than conduits; they are dynamically exchanging water and materials with surrounding terrestrial and subsurface environments. In fact, 96% of the carbon dioxide produced within river systems occurs in hyporheic zones, or sediment regions beneath and alongside a river where groundwater and river water mix. In fact, it is widely recognized that microbial processes in hyporheic zones, also known as the river’s liver, strongly influence the fate of carbon, nutrients, and contaminants transported through watersheds. Despite this general knowledge, observational studies linking nitrogen and carbon gas production to microbial activity, and studies of overall gas emissions, are limited across river systems, but especially absent from larger rivers. To identify microbial processes responsible for GHG emissions from large rivers, and to quantify their environmental drivers and dependencies, we selected the Columbia River as a representative field site. This is the largest river west of the North American Continental Divide, and produces a drainage and discharge roughly double of the Nile in Egypt. This river is also an important source for agricultural irrigation and generates more hydroelectric power than any other North American river. Dams manage the release of water in the river, with these changes in river discharge patterns strongly impacting microbial activities in hyporheic zones. Specifically, we target a region down-river from the Priest Rapids Dam (WA) associated with the PNNL SFA, where water discharge from the dam is highly controlled, resulting in 1.5 m fluctuations in river stage daily. This dynamic river stage creates an ideal research opportunity for investigating hydrological perturbations in GHG production, uptake, and emission from large river systems. In this exploratory 1-year project, we conducted data-driven field research to provide observations needed to support predictions about GHG flux from river ecosystems. Using porewater dialysis and surface flux measurements, we observed where and when GHGs were produced and is the emission impacted by river stage. Using meta-omics tools, we determined what microbial processes control production and consumption of GHG in these systems. Finally, we provided key observations to parameterize models that could predict the effect of environmental conditions on GHG emission in the Columbia River near shore environment, at the scale that the modeled processes operate. Closing knowledge gaps in the linkages between river stage, microbial processes, and GHG production and emission, as well as the spatial distribution and ecosystem dependencies of these processes, is necessary to better parameterize land surface models. We provide some of the first, detailed GHG measurements paired microbial activity from large river systems. Our findings have direct relevance to many other large rivers with flows altered by hydroelectric energy, reservoirs, snowmelt, tides, or agriculture.

54 ENVIRONMENTAL SCIENCES↗

Quantifying Pore Morphology in Spray-Formed Tantalum Using X-ray Micro-computed Tomography

In order to establish quantitative process–structure–property relationships in thermal spray coatings, a robust framework for defining (micro)structural characteristics is needed. Here, we present a quantitative characterization of the three-dimensional morphology of porosity in spray-formed tantalum samples based on high-resolution X-ray micro-computed tomography. Using synchrotron facilities, we acquired dozens of high-resolution scans, enabling a statistically meaningful comparison across multiple samples, different regions within samples, and spray processes. We quantify the spatial distribution, size, and topology of porous inclusions, with a significant focus on variability across samples and different spray processes (plasma and cold sprayed), as well as sensitivity to image segmentation and resolution. Based on a typical segmentation, we report porosities ranging from 0.9 to 1.7 pct for all samples tested, with significant sensitivity due to image segmentation resulting in estimates as low as 0.6 pct and as high as 4.8 pct. For the complex pore space morphology observed in these materials, we argue that a conventional analysis based on identifying individual pores is not well suited, and propose an alternative approach based on morphological metrics with a rich history in porous media literature, such as spatial correlations, local pore thickness, and scale-dependent sub-sampling. Spatial correlations indicate anisotropic splat structures, but only mildly anisotropic pores. Various measures of pore size show a wide distribution of sizes, ranging from sub-micron to 10-micron length scales. Scale-dependent variations in porosity suggest that representative volumes of several hundred microns are required for convergence of morphological metrics, with larger volumes for cold-spray materials. This work provides a robust quantitative basis for describing three-dimensional pore structure in thermal spray coatings.

36 MATERIALS SCIENCE↗

THE ROLE OF PEBBLE FRAGMENTATION IN PLANETESIMAL FORMATION. I. EXPERIMENTAL STUDY

Previous work on protoplanetary dust growth shows a halt at centimeter sizes owing to the occurrence of bouncing at velocities of ≳0.1 m s{sup −1} and fragmentation at velocities ≳1 m s{sup −1}. To overcome these barriers, spatial concentration of centimeter-sized dust pebbles and subsequent gravitational collapse have been proposed. However, numerical investigations have shown that dust aggregates may undergo fragmentation during the gravitational collapse phase. This fragmentation in turn changes the size distribution of the solids and thus must be taken into account in order to understand the properties of the planetesimals that form. To explore the fate of dust pebbles undergoing fragmenting collisions, we conducted laboratory experiments on dust-aggregate collisions with a focus on establishing a collision model for this stage of planetesimal formation. In our experiments, we analyzed collisions of dust aggregates with masses between 0.7 and 91 g mass ratios between target and projectile from 1 to 126 at a fixed porosity of 65%, within the velocity range of 1.5–8.7 m s{sup −1}, at low atmospheric pressure of ∼10{sup −3} mbar, and in free-fall conditions. We derived the mass of the largest fragment, the fragment size/mass distribution, and the efficiency of mass transfer as a function of collision velocity and projectile/target aggregate size. Moreover, we give recipes for an easy-to-use fragmentation and mass-transfer model for further use in modeling work. In a companion paper, we use the experimental findings and the derived dust-aggregate collision model to investigate the fate of dust pebbles during gravitational collapse.

79 ASTRONOMY AND ASTROPHYSICS↗

Global communication and control

A process detects media dispersion. The process detects the dispersion of contaminants through distributed remote sensor platforms that connect one or more sensors on a remote device. The process transmits detection data from the distributed remote sensor platform to a radiation tolerant satellite router. A gateway connects the radiation tolerant satellite router to a hardware server and converts the detection data to a compatible form with a protocol used by a hardware server. The process generates a plume model in response to the detection data and meteorological data that models dispersion plumes and activates and deactivates selected sensors in response to a forecasted to dispersion area.

Harter, Andrew↗

Extra Dimensions: 3D and Time in PDF Documentation

Experimental science is replete with multi-dimensional information which is often poorly represented by the two dimensions of presentation slides and print media. Past efforts to disseminate such information to a wider audience have failed for a number of reasons, including a lack of standards which are easy to implement and have broad support. Adobe's Portable Document Format (PDF) has in recent years become the de facto standard for secure, dependable electronic information exchange. It has done so by creating an open format, providing support for multiple platforms and being reliable and extensible. By providing support for the ECMA standard Universal 3D (U3D) file format in its free Adobe Reader software, Adobe has made it easy to distribute and interact with 3D content. By providing support for scripting and animation, temporal data can also be easily distributed to a wide, non-technical audience. We discuss how the field of radiation imaging could benefit from incorporating full 3D information about not only the detectors, but also the results of the experimental analyses, in its electronic publications. In this article, we present examples drawn from high-energy physics, mathematics and molecular biology which take advantage of this functionality. We demonstrate how 3D detector elements can be documented, using either CAD drawings or other sources such as GEANT visualizations as input.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Radiometric characterization process for locating radioactivity hold-up and measuring non-uniformly distributed sources - 15342

A novel approach to facility radiometric characterization for non-uniformly distributed radiation sources has been developed by ANTECH and is currently being employed at the Argonne National Laboratory. It is being applied to the measurement of a wide range of objects and the interiors and contents of laboratories and buildings. The integrated non destructive assay measurement process involves the use of a scanning spectroscopic gamma ray camera as well as high purity Germanium detectors with different collimators and hence differing detector fields of view to measure and quantify non-uniformly distributed sources of radioactivity in laboratory equipment, glove boxes, storage tanks, pipework and general laboratory objects and process components. The purpose of the radiometric characterization is to quantify radiation sources and radioactive contamination levels in facilities containing nuclear material and to ensure compliance with facility characterization requirements and for decontamination and decommissioning planning and execution. The integrated measurement process also involves automated data capture and processing software and a database with which both measurement instrument operators and subject matter expert analysts can interact. An appropriate measuring instrument (gamma camera, scintillation detector, high purity Germanium detector system, neutron detector or alpha-beta counter) records the measurement data and the measurement site is recorded in a photograph. All of the data relevant to the measurement (or set of measurements for a particular object) are recorded in the database including measured gamma ray spectra, equipment description and relevant calibration, measurement site photograph and measurement descriptive data. Once data analysis protocols are established for specific geometries and measurement situations they can be applied to the measured data in a partially automated manner, reducing analysis and transcription errors. In addition to measurements and data analysis the process also utilises radiometric modelling computer codes including MicroShield and Monte Carlo Neutrons Photons in order to perform model calculations where appropriate. In a typical measurement, the ANTECH RadSearch spectroscopic gamma camera is utilized to find areas of concentrated activity so that these areas can be measured and calculated separately from areas with diffuse contamination. The use of the gamma camera in scanning mode helps to ensure that the assay does not overestimate the detected radiation source, which is a frequent consequence of using standard far field geometry and attenuation analysis codes that assume uniformity of both density and radioactive source distribution. Once the hot spots are identified, high purity Germanium detector measurements are made using various collimators with wide or narrow fields of view as required. These measurements are then processed through a semi-automatic calculation process assuming an appropriate source configuration as identified by RadSearch. These source configurations include point sources, line sources or surface sources. The Generalized Geometry Hold-up algorithms are employed to determine an estimate of the activity of the various sources that are identified in a measurement. The ANTECH integrated measurement process provides an automated calculation procedure where the analyst can select the source type, geometry and appropriate input data, and then the software calculates the activity by radionuclide for all sources identified in the measurement. The measurement and analysis process is controlled under the ANTECH ISO9001: 2008 Quality Assurance System and managed by a site specific Quality Assurance Plan. Both Argonne National Laboratory and Department of Energy technical reviewers have vetted the Integrated Measurement and Analysis Process. The application of the measurement and analysis process is described and measurement examples are provided. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Multi-Fidelity Modeling and Control for Building Temperature Control

The ability to control energy loads such as a building's heating, ventilation, and air conditioning (HVAC) system can help facilitate increased penetration of variable renewable energy sources into the electric grid. To be able to control these HVAC systems more effectively, detailed simulations of the corresponding building physics is becoming increasingly important. These detailed simulations can be complex, nonlinear, and can require immense computational power when used in an advanced control method such as model predictive control (MPC), prompting the need to explore less computationally intensive strategies. In this work, a multi-fidelity approach is proposed to combine samples from a complex, high-fidelity model with a simple, low-fidelity model within the MPC control loop. More specifically, the parameters of a reduced-order, linear building model are periodically updated with knowledge from its high-fidelity counterpart - an EnergyPlus model - in an online fashion using a Gaussian Process surrogate model. Hence, highly accurate predictions of current and future conditions in a building are maintained with a substantially reduced computational burden compared to using the high-fidelity models alone. In other words, this linear parameter varying model preserves the low computational requirements of a low-order linear model while accurately modeling a building's dynamics. This allows a building controller to take highly informed actions without requiring a large computational budget.

building modeling↗

Energy and Cost Optimized Technology Options to Meet Energy Needs of Food Processors

Full Paper Submission for: Combined cooling, heating and electric power (CCHP) distributed generation (DG) systems can provide electric power and, heating and cooling capability to commercial and industrial facilities directly onsite, while increasing energy efficiency, security of energy supply, grid independence and enhancing the environmental and economic situation for the site. Food processing industries often have simultaneous requirements for heat, steam, chilling and electricity making them well suited for the use of such systems to supply base-load or as peak reducing generators enabling reduction of overall energy use intensity. This paper documents analysis from a project evaluating opportunities enabled by CCHPDG for emission and cost reductions and energy storage systems installed onsite at food processing facilities. In addition, this distributed generation coupled with energy storage demonstrates a non-wires solution to delay or eliminate the need for upgrades to electric distribution systems. It was found that a dairy processing plant in the Pacific Northwest currently purchasing 15,000 MWh/yr of electricity and 190,000 MMBtu/yr of gas could be provided with a 1.1 MW CCHP system reducing the amount of electric power purchased to 450 MWh/yr while increasing the gas demand to 255,000 MMBtu/yr. The high percentage of hydro-power in this region resulted in CO2 emissions from CCHP to be higher than that attributed to the electric utility/regional energy mix. The value of this work is in documenting a real-world example demonstrating the value of CCHP to facility owners and financial decision makers to encourage them to more seriously consider CCHP systems when building or upgrading facilities.

Makhmalbaf, Atefe↗

Universality of Unintegrated Gluon Distributions at small x

We systematically study dijet production in various processes in the small-x limit and establish an effective kt-factorization for hard processes in a system with dilute probes scattering on a dense target. In the large-Nc limit, the unintegrated gluon distributions involved in different processes are shown to be related to two widely proposed ones: the Weizsacker-Williams gluon distribution and the dipole gluon distribution.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Thermodynamic Implementations of Quantum Processes

Abstract Recent understanding of the thermodynamics of small-scale systems have enabled the characterization of the thermodynamic requirements of implementing quantum processes for fixed input states. Here, we extend these results to construct optimal universal implementations of a given process, that is, implementations that are accurate for any possible input state even after many independent and identically distributed (i.i.d.) repetitions of the process. We find that the optimal work cost rate of such an implementation is given by the thermodynamic capacity of the process, which is a single-letter and additive quantity defined as the maximal difference in relative entropy to the thermal state between the input and the output of the channel. Beyond being a thermodynamic analogue of the reverse Shannon theorem for quantum channels, our results introduce a new notion of quantum typicality and present a thermodynamic application of convex-split methods.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Radial behavior of the pulsed dielectric-barrier discharge in atmospheric helium

The radial behavior of the pulsed dielectric-barrier discharge in atmospheric helium is studied by a two-dimensional, self-consistent fluid model. It is found that the two discharges ignited during one voltage pulse can possess different radial behaviors, and the discharge behavior is determined by the electron density distribution right before this discharge is ignited. The electron density distributions before the two discharges start depend on the time intervals between two discharges and their previous discharge processes. If the electron density distribution is radially uniform at the end of the previous discharge, the shorter the time interval between two discharges is, the more uniform the electron density distribution before the next discharge is, and thus the more homogenous the subsequent discharge becomes. In pulsed discharge, the time intervals between two discharges are mainly determined by the duration and repetition frequency of applied voltage pulse. These results are further supported by the investigation of the discharge behaviors under different pulse durations and repetition frequencies.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Explicit temperature treatment in Monte Carlo neutron tracking routines - First results

This article discusses the preliminary implementation of the new explicit temperature treatment method to the development version Monte Carlo reactor physics code Serpent 2 and presents the first practical results calculated using the method. The explicit temperature treatment method, as introduced in [1], is a stochastic method for taking the effect of thermal motion into account on-the-fly in a Monte Carlo neutron transport calculation. The method is based on explicit treatment of the motion of target nuclei at collision sites and requires cross sections at 0 K temperature only, regardless of the number of temperatures in the problem geometry. The method includes a novel capability of modelling continuous temperature distributions. Test calculations are performed for two test cases, a PWR pin-cell and a HTGR system. The resulting k{sub eff} and flux spectra are compared to a reference solution calculated using Serpent 1.1.16 with Doppler-broadening rejection correction [2]. The results are in very good agreement with the reference and also the increase in calculation time due to the new method is on acceptable level although not fully insignificant. On the basis of the current study, the explicit treatment method can be considered feasible for practical calculations. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Physics-Informed Gaussian Process Regression for States Estimation and Forecasting in Power Grids

Real-time state estimation and forecasting are critical for the efficient operation of power grids. In this paper, a physics-informed Gaussian process regression (PhI-GPR) method is presented and used for forecasting and estimating the phase angle, angular speed, and wind mechanical power of a three-generator power grid system using sparse measurements. In standard data-driven Gaussian process regression (GPR), parameterized models for the prior statistics are fit by maximizing the marginal likelihood of observed data. In the PhI-GPR method, we propose to compute the prior statistics offline by solving stochastic differential equations (SDEs) governing the power grid dynamics. The short-term forecast of a power grid system dominated by wind generation is complicated by the stochastic nature of the wind and the resulting uncertainty in wind mechanical power. Here, we assume that the power grid dynamics are governed by swing equations, with the wind mechanical power fluctuating randomly in time. We solve these equations for the mean and covariances of the power grid states using the Monte Carlo simulation method. We demonstrate that the proposed PhI-GPR method can accurately forecast and estimate observed and unobserved states. For the considered problem, PhI-GPR has computational advantages over the ensemble Kalman filter (EnKF) method: In PhI-GPR, ensembles are computed offline and independently of the data acquisition process, whereas for EnFK, ensembles are computed online with data acquisition, rendering real-time forecast more challenging. We also demonstrate that the PhI-GPR forecast is more accurate than the EnKF forecast when the random mechanical wind power is non-Markovian. In contrast, the two methods produce similar forecasts for the Markovian mechanical wind power. For observed states, we show that PhI-GPR provides a forecast comparable to the standard data-driven GPR; both forecasts are significantly more accurate than the autoregressive integrated moving average (ARIMA) forecast. We also show that the ARIMA forecast is more sensitive to observation frequency and measurement errors than the PhI-GPR forecast.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Metabolic characterization of anaerobic fungi provides a path forward for bioprocessing of crude lignocellulose

The conversion of lignocellulose-rich biomass to bio-based chemicals and higher order fuels remains a grand challenge, as single-microbe approaches often cannot drive both deconstruction and chemical production steps. In contrast, consortia based bioprocessing leverages the strengths of different microbes to distribute metabolic loads and achieve process synergy, product diversity, and bolster yields. Here, we describe a biphasic fermentation scheme that combines the lignocellulolytic action of anaerobic fungi isolated from large herbivores with domesticated microbes for bioproduction. When grown in batch culture, anaerobic fungi release excess sugars from both cellulose and crude biomass due to a wealth of highly expressed carbohydrate active enzymes (CAZymes), converting as much as 49% of cellulose to free glucose. This sugar-rich hydrolysate readily supports growth of Saccharomyces cerevisiae, which can be engineered to produce a range of value-added chemicals. Further, construction of metabolic pathways from transcriptomic data reveals that anaerobic fungi do not catabolize all sugars that their enzymes hydrolyze from biomass, leaving other carbohydrates such as galactose, arabinose, and mannose available as nutritional links to other microbes in their consortium. Although basal expression of CAZymes in anaerobic fungi is high, it is drastically amplified by cellobiose breakout products encountered during biomass hydrolysis. Overall, these results suggest that anaerobic fungi provide a nutritional benefit to the rumen microbiome, which can be harnessed to design synthetic microbial communities that compartmentalize biomass degradation and bioproduct formation.

Environmental Molecular Sciences Laboratory↗

Short-Term Load Forecasting Considering EV Charging Loads with Prediction Interval Evaluation

Short-term load forecasting plays a critical role in power system planning and operation. Along with the electrification of various loads, electricity demands are becoming increasingly hard to predict. Notably, the recent rise in electric vehicles (EVs) has further contributed to this unpredictability. To address this issue, this paper proposes a probabilistic load forecasting strategy utilizing Gaussian process regression, structured in a day-ahead manner. While many works focus on deterministic prediction, probabilistic forecasting offers additional insights into variability and uncertainty, enabling more flexible and reliable operation for power systems. To enhance the accuracy of the load forecasting model, the inputs include features related to EV charging habits as well as commonly used weather information. The load forecasting results are evaluated using various metrics, including conventional ones that assess the accuracy of point forecasts, as well as additional metrics that test the reliability of prediction intervals. The proposed load forecasting method is finally tested on real residential power consumption data and EV charging data sampled from real-world sources. The results prove that the new features can greatly improve the performance of the load forecasting method.

electrical vehicle↗

Process and feedstock driven microstructure for laser powder bed fusion of 316L stainless steel

Here in the pursuit of improving additively manufactured (AM) component quality and reliability, fine-tuning critical process parameters such as laser power and scan speed is a great first step toward limiting defect formation and optimizing the microstructure. However, the synergistic effects between these process parameters, layer thickness, and feedstock attributes (e.g. powder size distribution) on part characteristics such as microstructure, density, hardness, and surface roughness are not as well-studied. In this work, we investigate 316L stainless steel density cubes built via laser powder bed fusion (L-PBF), emphasizing the significant microstructural changes that occur due to altering the volumetric energy density (VED) via laser power, scan speed, and layer thickness changes, coupled with different starting powder size distributions. This study demonstrates that there is not one ideal process set and powder size distribution for each machine. Instead, there are several combinations or feedstock/process parameter ‘recipes’ to achieve similar goals. This study also establishes that for equivalent VEDs, changing powder size can significantly alter part density, GND density, and hardness. Through proper parameter and feedstock control, part attributes such as density, grain size, texture, dislocation density, hardness, and surface roughness can be customized, thereby creating multiple high-performance regions in the AM process space.

316L stainless steel↗

SU-C-BRB-03: Novel Technique to Implement GRID Therapy in a Commercial Treatment Planning System

Purpose: Due to the difficulty of creating an accurate multi-aperture block in treatment planning systems (TPS), spatially fractionated radiation therapy (GRID Therapy) is limited to clinical setups using simple monitor unit calculations. In this work, we present a novel approach to accurately model a grid block in a TPS and evaluate its dosimetric accuracy. Methods: An acrylic GRID block was fashioned such that the hole size and spacing were the same dimensions of the brass GRID block (dotDecimal) at isocenter. Holes were non-divergent and drilled 1 cm deep. CT scans (RT Lightspeed, GE Healthcare) of the block were acquired using axial slices with a thickness of 0.625 mm. These were imported into Eclipse (version 11, Varian Medical Systems) and holes were auto-contoured into structures. The GRID block was created in Eclipse by forming apertures around the hole structures using a simple three step process. Treatment plans were created in a water-equivalent phantom using 6 and 10 MV beams, delivered with a linear accelerator (Clinac iX, Varian Medical Systems) and measured using a water tank, film and a diode array. Results: Comparisons of percent depth dose curves and profiles at depths of maximum dose, 5cm, and 10cm for field sizes ranging from 5 cm{sup 2} to 25 cm{sup 2} using water and film were within 3% of their respective positions in Eclipse. GRID plans utilizing various jaw sizes and MLC blocking measured with film and a diode array showed pass rates with an average of 97% using Gamma analysis at 3%/ 3 mm and no lower than 93%. Conclusion: We have developed a simple yet accurate method of incorporating a GRID block into a commercially available TPS and demonstrated that the TPS accurately reflects the dose delivered. This will allow for real-time planning using patient scans and encourage new methods for GRID therapy. Support provided by dotDecimal, Sanford, FL.

60 APPLIED LIFE SCIENCES↗