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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Plutonium Speciation and Oxidation State Distributions in the Presence of Citrate

We explored the speciation and kinetics of the Pu(VI)-citrate and Pu(III)-citrate systems (pH m = 2.5–11.0, I = 0.1 M NaCl, T = 23 °C, O 2 (g) < 2 ppm) using ultraviolet–visible–near-infrared (UV–vis–NIR) spectrophotometry, solvent extraction, and PHREEQC modeling. Formation constants were determined for PuO 2 (HcitH)(aq) (log K° 1,1 = 1.09 ± 0.05) and PuO 2 (HcitH)(citH) 3– (log K° 1,2 = -0.20 ± 0.07), and evidence for (PuO 2 ) m (citH –k ) n (OH) x 2m(3+k)n–x was identified under alkaline conditions. Pu(VI) species were found to be less stable in the presence of citrate than in the absence of citrate (t ≤ 168 days); the rate of reduction increased with increasing pH. Further, the direct reduction of Pu(VI) to Pu(IV) was required to fit experimental data in the presence of citrate but did not improve the fit for Pu in the absence of citrate. We also observed increased Pu(III) stability in the presence of citrate (t ≤ 293 days), with higher concentrations of Pu(III) favored at lower pH. Finally, we provide evidence of a radiolysis-driven mechanism for the citrate-mediated reduction of plutonium that involves electron transfer from the oxidative breakdown of citrate. Our work highlights the need to investigate the redox effect of organic ligands on plutonium oxidation states under repository-relevant conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Confidentiality-preserving machine learning algorithms for soft-failure detection in optical communication networks

Automated fault management is at the forefront of next-generation optical communication networks. The increase in complexity of modern networks has triggered the need for programmable and software-driven architectures to support the operation of agile and self-managed systems. In these scenarios, the European Telecommunications Standards Institute zero-touch network and service management approach is imperative. The need for machine learning algorithms to process the large volume of telemetry data brings safety concerns as distributed cloud-computing solutions become the preferred approach for deploying reliable communication network automation. This paper’s contribution is twofold. First, we propose a simple yet effective method to guarantee the confidentiality of the telemetry data based on feature scrambling. The method allows the operation of third-party computational services without direct access to the full content of the collected data. Additionally, the effectiveness of four unsupervised machine learning algorithms for soft-failure detection is evaluated when applied to the scrambled telemetry data. The methods are based on factor analysis, principal component analysis, nonlinear principal component analysis, and singular value decomposition. Most dimensionality reduction algorithms have the common property that they can maintain similar levels of fault classification performance while hiding the data structure from unauthorized access. Evaluations of the proposed algorithms demonstrate this capability.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Thermal loading effects on chalk hydromechanical behavior for nuclear waste disposal

Safe disposal of heat-generating nuclear waste depends on host rock stability under thermal, hydrological, and mechanical stresses. This study investigates the effect of thermal loading on mechanical behavior of the shallowly buried Ghareb formation chalk through triaxial and hydrostatic constant strain rate and creep tests at temperatures up to 100 ˚C and effective pressures up to 20.7 MPa. Experimental results show that thermal loading reduces the elastic moduli of chalk by 50–75%, and a transition occurs above 60 ˚C where creep rates increase rapidly. Water saturation nearly doubles the thermally induced strain compared to dry conditions and strongly decreases material rigidity. Thermal loading also leads to significant pore pressure increases under undrained conditions and leads to reductions in the apparent permeability during drained conditions. Laboratory experimental data were used to parameterize and develop a preliminary constitutive model for predicting future deformation during repository operations in the Ghareb. The strongly coupled effects – mechanical weakening, fluid pressure fluctuations, and permeability modification – demonstrate that elevated repository temperatures will have a pronounced effect on the near field Ghareb behavior during waste disposal operations. The findings indicate that the coupled interactions must be considered in predictive models and repository design to ensure long-term nuclear waste isolation and safety.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Low-energy magneto-optics of Tb 2 Ti 2 O 7 in a [111] magnetic field

The pyrochlore magnet Tb 2 Ti 2 O 7 shows a lack of magnetic order to low temperatures and is considered to be a quantum spin liquid candidate. Here we perform time-domain THz spectroscopy on high-quality Tb 2 Ti 2 O 7 crystals and study the low-energy excitations as a function of [111] magnetic field with high energy resolution. The low-energy crystal-field excitations change their energies anomalously under magnetic field. Despite several sharp field-dependent changes, we show that the material's spectrum can be described not by phase transitions but by field-dependent hybridization between the low-energy crystal-field levels. We highlight the strong coupling between spin and lattice degrees of freedom in Tb 2 Ti 2 O 7 as evidenced by the magnetic-field tunable crystal-field environment. Calculations based on single ion physics with field-induced symmetry reduction of the crystal-field environment can reproduce our data.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Integrated Metrics for County-Level Resilience Ranking Using Entropy and TOPSIS

In the face of atypical weather events, power infrastructure failures, and limited resources for resilience investment, energy decision-makers need data-driven metrics to allocate resilience investments and maximize the reduction of power outage impacts. For state-level planning, for instance, ranking the resilience of each county is key to ensuring effective distribution of resources. In such cases, resilience for each spatial unit is multifaceted and is captured by a set of indicators (i.e., metrics) that can be combined into an overall score that reduces the complexity of power outage dynamics to a single decision metric. However, weighting of these indicators is often addressed by simplifying assumptions (i.e., equal weights) or semi-subjective methods that rely on user-defined weights that can introduce biases (e.g., weighted average score). Within the disaster risk reduction and resilience engineering community, a recurring challenge in multicriteria decision-making is the objective weighting of indicators for composite indices. To address this issue, we have leveraged a Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) combined with an entropy-based weighting approach to calculated the integrated scores. This method objectively determines the importance of each metric, better discerns between spatial units (i.e., counties), and offers a more reliable ranking of counties according to their relative resilience attributes. By improving methods for integrating resilience indicators, our approach helps planners and decision-makers prioritize resources more effectively for more efficient resilience investments.

Bhusal, Narayan [Oak Ridge National Laboratory (OR↗
DOE OSTI2025

Modeling MTS pyrolysis and SiC deposition kinetics using principal component analysis and neural networks

Accurate chemical kinetics modeling is crucial for improving the efficiency of chemical processing and synthesis of ceramic matrix composites. Detailed kinetic models are computationally expensive due to the large number of transported chemical species, while the simplified physics-based models, such as single-step global mechanisms, are efficient but often overlook key chemical intermediates and pathways. Recent deep learning approaches promise accurate and cost-effective models. Yet, they require additional closures for the transported nonlinear latent variables, complicating integration with existing solvers. In this work, we develop a hybrid linear—nonlinear reduced model for silicon carbide deposition from methyltrichlorosilane precursor by combining principal component analysis (PCA) and autoencoder (AE) neural network (NN) approaches. PCA is used to identify a smaller set of linear transport variables, enabling direct reuse of conventional transport solvers. NNs then reconstruct the full chemical state from these reduced variables. We demonstrate the method on a chemical vapor deposition reactor—comprising a gas-phase pyrolysis plug flow reactor and a heterogeneous surface reactor—over a wide range of temperatures, pressures, and residence times. Our PCA–AE model achieves high accuracy with only five transported scalars, achieving an eightfold cost reduction compared to detailed mechanisms, in both a priori (using data from the test set only) and a posteriori (coupled with a differential equation solver). In conclusion, notable errors arise primarily near training domain boundaries and for long residence times, indicating the need for domain shift indicators and better long-horizon predictions in future reduced chemistry model development.

autoencoder neural networks↗
DOE OSTI2022

Optimization of the deep neural network parameters for generating homogenized fuel assembly data for nodal codes

Homogenized fuel assembly (FA) data is a typical input data for nodal codes. Generating that data, however, could be time-consuming. One of promising ways to mitigate the computational burden of generating macroscopic cross-sections is to use trained artificial neural network (ANN) models for predicting nuclear data. However, there is a challenge to make the model support variable FA geometry. In this work, two most common types of FA were combined in one ANN model. Since there could be multiple ways of converting 2-dimensional FA data into 1-dimensional input vector for ANN, three different approaches of data flattening were evaluated. The input parameters included each fuel pin enrichment, fuel temperature, moderator temperature and boron concentration. The output parameters were 2-group macroscopic cross-sections (XS) and pin power distribution (HFF). A fully connected deep neural network (DNN) model was trained and tested using pre-generated data obtained with lattice physics code STREAM. The results of this study showed no statistically significant difference in the accuracy of XS and HFF generation for all 3 tested input vector orders. This means that fully connected DNN for XS generation demonstrated input sequence invariance. Results of comparing predicted XS data with reference solutions were found sufficiently close considering the reduction of computation time offered by ANN. Mean relative difference (MRD) for all output XS parameters was found below 0.7%, while HFF MRD was found higher compared to XS values, in some cases slightly exceeding 1%, mostly near guide tube locations. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Detecting the undetected: Dealing with non-routine events using advanced M&V meter-based savings approaches

In a rapidly evolving energy industry, utilities are dealing with new challenges like integrating distributed energy resources and market saturation for advanced lighting retrofits. Demand-side management programs require new approaches to meet aggressive carbon reduction goals. Advanced measurement & verification (M&V) is an energy data analysis method using smart meter data in combination with analytics to quantify energy efficiency project savings. Advanced M&V shows great promise for supporting next generation commercial programs including retro commissioning, multi-measure retrofits, and behavior change programs. Advanced M&V captures real project impacts at the meter, but sometimes non-project events can also impact consumption (so-called “non-routine events” [NREs]). Accurately detecting and accounting for NREs is important for reducing uncertainty of savings estimates and helps manage investment risk for different stakeholders (e.g., utilities, building owners, ESCOs). Recent research has shown promise in establishing data-driven techniques to identify and adjust for NREs, but fundamental questions still remain, such as: how can you distinguish NREs from acceptable noise in energy consumption profiles? What is the frequency and magnitude of NREs? Can their detection and adjustment be automated and streamlined? This paper documents the state of the art in NRE quantification and analysis. The results of research to quantify the frequency, nature and direction of NREs, and methods and metrics for determining a trigger threshold for taking action on NREs are presented. The paper also documents the latest technical guidance on application of NRE detection and adjustment methods.

Fernandes, Samuel↗
DOE OSTI2025

Characterization and Quantification of Radiation-Induced Clusters/Precipitates in RPV Steels Using STEM-EDS and Machine Learning

Over the operational lifespan of a nuclear reactor, reactor pressure vessel (RPV) steels are subjected to significant neutron irradiation, resulting in complex microstructural changes and the consequent degradation of mechanical properties. Various physically motivated correlation models have been developed to predict neutron irradiation-induced embrittlement of RPVs under different irradiation conditions. However, the efficient and accurate characterizations and quantification of radiation-induced clusters in RPVs are still challenging, which will affect the precision of the predictive models for embrittlement of RPV components. In the DOE Visiting Faculty Program (VFP) research work at Oak Ridge National Lab (ORNL), I integrate machine learning to aid Scanning Transmission Electron Microscopy – Energy Dispersive X-ray Spectroscopy (STEM-EDS) analyses, which improve the characterization and quantification of radiation-induced clusters in RPV steels, thereby enabling more accurate predictions of material behavior under irradiation. The surveillance base- and welded- RPV steels were annealed at various temperatures of 340 °C, 450 °C and 500 °C for up to 168 hours, respectively. Afterwards, I have characterized radiation-induced clusters using advanced STEM-EDS techniques and subsequently applying machine learning algorithms to analyze and refine STEM-EDS datasets, enhancing the quantification of clusters compositions and distributions. In the end, an efficient workflow for integrating STEM-EDS data analysis with machine learning to address challenges including noise reduction has been developed. The completion of this VFP work will support bridge critical gaps in the accurate quantification of radiation-induced clusters in RPV steels using STEM-EDS and support the development of more precise models for predicting RPV embrittlement in the Light Water Reactor Sustainability program supported by Department of Energy and enhancing the collaboration between ORNL and Alred University. The outcome of the VFP project will leverage a few research papers submission to peer-reviewed journals in the relevant scientific field and a few oral presentations at national and international conferences.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Strategies for Flood Resilience and Grid Investment Among Iowa's Electric Distribution Utilities

Flooding poses a growing threat to Iowa's electric distribution system, yet utilities face significant data and modeling challenges in planning effective resilience investments. This report provides a foundational assessment of how distribution utilities in Iowa, investor-owned, municipal, and cooperative, approach resilience planning, with a focus on flood risk. It combines hazard characterization, review of state and utility practices, and application of NLR's Energy Resilience Analysis for Distribution Systems (ERAD) and Capacity Expansion Decision Support for Distribution Networks (CADET) tools. Using FEMA floodplains, Iowa Flood Center depth grids, and utility infrastructure data, the analysis quantifies asset exposure, simulates outage risks, and evaluates resilience strategies such as pole hardening, undergrounding, and substation protection. Results indicate that while feeder-level upgrades provide incremental benefits, the most significant reductions in outage risk are achieved through targeted substation interventions. The report highlights key data gaps, such as limited elevation data and fragility functions, and underscores the need for probability-weighted investment frameworks to address both frequent and catastrophic flood events. These insights aim to support utilities, state agencies, and federal partners in prioritizing resilience investments that safeguard Iowa's electric grid against future flooding.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Probabilistic projections of the Amery Ice Shelf catchment, Antarctica, under conditions of high ice-shelf basal melt

Abstract. Antarctica's Lambert Glacier drains about one-sixth of the ice from the East Antarctic Ice Sheet and is considered stable due to the strong buttressing provided by the Amery Ice Shelf. While previous projections of the sea-level contribution from this sector of the ice sheet have predicted significant mass loss only with near-complete removal of the ice shelf, the ocean warming necessary for this was deemed unlikely. Recent climate projections through 2300 indicate that sufficient ocean warming is a distinct possibility after 2100. This work explores the impact of parametric uncertainty on projections of the response of the Lambert–Amery system (hereafter “the Amery sector”) to abrupt ocean warming through Bayesian calibration of a perturbed-parameter ice-sheet model ensemble. We address the computational cost of uncertainty quantification for ice-sheet model projections via statistical emulation, which employs surrogate models for fast and inexpensive parameter space exploration while retaining critical features of the high-fidelity simulations. To this end, we build Gaussian process (GP) emulators from simulations of the Amery sector at a medium resolution (4–20 km mesh) using the Model for Prediction Across Scales (MPAS)-Albany Land Ice (MALI) model. We consider six input parameters that control basal friction, ice stiffness, calving, and ice-shelf basal melting. From these, we generate 200 perturbed input parameter initializations using space filling Sobol sampling. For our end-to-end probabilistic modeling workflow, we first train emulators on the simulation ensemble and then calibrate the input parameters using observations of the mass balance, grounding line movement, and calving front movement with priors assigned via expert knowledge. Next, we use MALI to project a subset of simulations to 2300 using ocean and atmosphere forcings from a climate model for both low- and high-greenhouse-gas-emission scenarios. From these simulation outputs, we build multivariate emulators by combining GP regression with principal component dimension reduction to emulate multivariate sea-level contribution time series data from the MALI simulations. We then use these emulators to propagate uncertainty from model input parameters to predictions of glacier mass loss through 2300, demonstrating that the calibrated posterior distributions have both greater mass loss and reduced variance compared to the uncalibrated prior distributions. Parametric uncertainty is large enough through about 2130 that the two projections under different emission scenarios are indistinguishable from one another. However, after rapid ocean warming in the first half of the 22nd century, the projections become statistically distinct within decades. Overall, this study demonstrates an efficient Bayesian calibration and uncertainty propagation workflow for ice-sheet model projections and identifies the potential for large sea-level rise contributions from the Amery sector of the Antarctic Ice Sheet after 2100 under high-greenhouse-gas-emission scenarios.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Technology Scouting Report: Reliability Implications of Recent PV Module Technology Trends

This webinar links recent and anticipated PV module technology trends - such as the transition to n-type cells, increasing module sizes, and new encapsulants - with potential increases and reductions in reliability risk. Market trends are drawn from industry data and projections, and corresponding reliability implications are synthesized from interviews with researchers and industry experts. The webinar is an interim step toward developing a technology scouting report.

forecast↗
DOE OSTI2023

System and method for high dynamic range waveform digitization

Diverse applications from particle physics experiments to lidar are driving cost and current reduction in giga-hertz sampling rate high-resolution data conversion. Multiple imagers captures a single pixel of data and require processing at very high speed. High-bandwidth high-rate signal sampling, analog-to-digital conversion, and transfer of large amounts of data to a digital data acquisition block are required in such systems. Dynamic range, power consumption, and transfer of high-speed, high-bit width data are key implementation challenges. Data acquisition architectures optimized for specific requirements of such systems may facilitate system implementation and reduce overall system cost.

Mostafanezhad, Isar↗
DOE OSTI2024

Shallow Geothermal Resources for Cooling Applications at the University of Hawai‘i

Scientists at Berkeley Lab have teamed up with The University of Hawai‘i at Manoa (UH Manoa) through the U.S. Department of Energy’s (DOE’s) Energy Transitions Initiative Partnership Project (ETIPP) to evaluate the technological and market feasibility of shallow geothermal heat exchanger (GHE) technology for building cooling, energy efficiency, and emissions reduction applications in Hawai’i. The team is assessing the data necessary to model the feasibility of deploying this technology, the actual models that will be used, and what hurdles need to be overcome to install a demonstration case. UH has an abundance of geologic and geothermal data and is looking to the national labs’ expertise to execute this analysis. UH is also interested in investigating policy, regulatory, and business conditions advantageous for implementation of a pilot project, and more broad deployment of this technology in Hawai‘i. In many locations around the world, the demands for heating and cooling are roughly balanced over the course of the year, so GHEs do not cause significant long-term changes in subsurface temperature. This is not the case in Hawai’i, where the demand for heating is very small, meaning that over time, GHEs will add heat to the subsurface. If temperatures increase significantly, GHE systems will not work as designed. Regional groundwater flow has the potential to sweep heated water away from boreholes, thereby maintaining the functionality of the GHE system. Significant regional groundwater flow requires two things: a sufficiently large driving hydraulic head gradient (usually closely related to surface topography), and sufficient porosity and permeability to enable groundwater to flow in large enough quantities to enable near-borehole temperatures to be maintained at ambient values. Hawai‘i’s volcanic terrain offers ample surface topographic variation. The lava itself shows an extremely large range of porosity and permeability, making it crucial to select sites with large enough values of these properties. Numerical modeling of coupled groundwater and heat flow can be used to determine how large is large enough. Both closed-loop and open-loop systems are being investigated. Another option being considered is using cool seawater as the source of chill. Currently work is progressing on two fronts. A hydrogeologic model for a closed-loop system is being developed for the Stan Sheriff Center at the UH Manoa campus, where a subsurface karst system immediately downgradient of the borefield may provide efficient removal of heated groundwater. The team will also develop a technoeconomic model for this site to compare the cost of cooling using a GHE system with the costs of operating the current air conditioning system. At the state scale, geographic information system (GIS) layers of various attributes relevant for GHE are being combined to develop an overall favorability map for employing GHE in Hawai‘i.

Doughty, C↗
DOE OSTI2020

A CALPHAD-informed approach to modeling constituent redistribution in Zr-based metallic fuels using BISON

Here, a CALPHAD-informed (Computer Coupling of Phase Diagrams and Thermochemistry) constituent redistribution model was developed for Zr-based metallic fuels and incorporated into the BISON fuel performance code. Three uncertain model parameters associated with β and γ phase kinetics were calibrated using integral test data from U-Zr fuel elements irradiated in Experimental Breeder Reactor II. The calibrated constituent redistribution model was shown to predict the behavior of U-Zr fuels with excellent accuracy. Model predictions for U-Pu-Zr fuels were physically reasonable but less accurate. Reduction of uncertainties in the ternary phase transition temperatures and collection of kinetic data for the ζ phase are expected to improve the model’s ternary predictions. Finally, the new model was coupled to existing thermomechanics models in BISON to simulate irradiation of an entire U-Zr fuel element, demonstrating its ability to accurately predict the behavior of U-Zr fuels with realistic geometries and mesh resolutions at the engineering scale.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

The Calar Alto Legacy Integral Field Area survey: extended and remastered data release

ABSTRACT This paper describes the extended data release (eDR) of the Calar Alto Legacy Integral Field Area (CALIFA) survey. It comprises science-grade quality data for 895 galaxies obtained with the Potsdam Multi Aperture Spectograph/PPak instrument at the 3.5-m telescope at the Calar Alto Observatory along the last 12 yr, using the V500 setup [3700–7500 Å, 6 Å/full-width at half-maximum (FWHM)] and the CALIFA observing strategy. It includes galaxies of any morphological type, star formation stage, a wide range of stellar masses (∼107–1012 M⊙), at an average redshift of ∼0.015 (90 per cent within 0.005 < z < 0.05). Primarily selected based on the projected size and apparent magnitude, we demonstrate that it can be volume corrected resulting in a statistically limited but representative sample of the population of galaxies in the nearby Universe. All the data were homogeneous re-reduced, introducing a set of modifications to the previous reduction. The most relevant is the development and implementation of a new cube-reconstruction algorithm that provides with an (almost) seeing-limited spatial resolution (FWHMPSF ∼ 1.0 arcsec). To illustrate the usability and quality of the data, we extracted two aperture spectra for each galaxy (central 1.5 arcsec and fully integrated), and analyse them using pyFIT3D. We obtain a set of observational and physical properties of both the stellar populations and the ionized gas, that have been compared for the two apertures, exploring their distributions as a function of the stellar masses and morphologies of the galaxies, comparing with recent results in the literature.

Sánchez, S. F. (ORCID:0000000164449307)↗
DOE OSTI2024

Search for proton decay via 𝑝 → 𝑒 + ⁢𝜂 and 𝑝 → 𝜇 + ⁢𝜂 with a 0.37 Mton-year exposure of Super-Kamiokande

A search for proton decay into 𝑒 + /𝜇 + and a 𝜂 meson has been performed using data from a 0.373 Mton·year exposure (6050.3 live days) of Super-Kamiokande. Compared to previous searches this work introduces an improved model of the intranuclear 𝜂 interaction cross section, resulting in a factor of 2 reduction in uncertainties from this source and ∼10% increase in signal efficiency. No significant data excess was found above the expected number of atmospheric neutrino background events resulting in no indication of proton decay into either mode. Lower limits on the proton partial lifetime of 1.4 × 10 34 years for 𝑝 → 𝑒 + ⁢𝜂 and 7.3 × 10 33 years for 𝑝 → 𝜇 + ⁢𝜂 at the 90% CL were set. These limits are around 1.5 times longer than our previous study and are the most stringent to date.

Grand unified models↗
DOE OSTI2024

Ducted Fuel Injection And Cooled Spray Technologies For Particulate Control In Heavy-duty Diesel Engines (Final Report)

Cooled Spray (CS) and Ducted Fuel Injection (DFI) are in-cylinder technologies for diesel engines that can reduce particulate matter and soot emissions and data has been published showing that these technologies can reduce soot emissions by 75-100% for some engines at some operating conditions. However, little is known about scaling the devices for engine size. Additionally, the performance of either technology over the engine duty cycle has not been explored. This project addresses both of these points through single-cylinder engine investigations. The objectives of this project are to provide details about dimensional scaling of these devices and to demonstrate 75% PM reduction over a range of operating conditions on a single-cylinder engine. Two engines were used for this project: a 125mm bore optically accessible engine at Sandia National Laboratories and a 168mm bore metal engine at Southwest Research Institute. The optical engine was used to study the performance of DFI and CS inserts for a large injector orifice diameter injector that is characteristic of a locomotive engine and to perform scaling studies for DFI. The metal engine was used to perform scaling and alignment studies for CS and to evaluate the technology for both EGR and non-EGR engines over the engine operating map. Modifications were required for both engines to accept the prototype inserts being tested. The optical engine required a new fuel injector, cylinder head and piston so that tests could be run at the pressures and engine speeds required. Additionally, a novel rotating stage was designed for the optical engine to simplify alignment of the modules. The metal engine required a modified cylinder head to accept CS inserts and a modified piston to provide additional space around the fuel injector for the CS inserts. Tests on the optical engine showed that DFI reduces PM emissions for both small injector orifices (0.170mm diameter) and large injector orifices (0.290mm). For high load testing, the DFI modules were not as effective as at low load testing, but it was acknowledged that minimal geometric optimization was performed and more improvements may be possible. Comparing DFI to CS and conventional diesel combustion (CDC), DFI performed better than CS or CDC. The CS geometries used in these studies may not be ideal for that engine and additional modifications likely would improve performance. Tests on the metal engine showed PM reductions as high has 80% at some operating conditions with duty-cycle PM reductions of ~50% for EGR and non-EGR configurations. The CS testing on the metal engine showed that chamfering of the fuel passage inlet either through hydro-erosion or mechanical grinding provided significant improvements in the PM reduction capabilities of the insert. Additionally, alignment sensitivities were explored and the data show that the tolerance to misalignment is approximately 0.05 to 0.1mm for the inserts that were studied here. Air-fuel ratio was shown to be important in the effectiveness of the CS inserts. In several tests, it was shown that the CS inserts are more effective at reducing the PM for high-AFR operating conditions compared to low AFR conditions. In summary, multiple designs were evaluated on both engines. It was found that for the conditions and configurations studied here, a fuel passage diameter of ~2.5mm performed best overall. Significant duty-cycle PM reductions are possible using these technologies and sensitivities to AFR, alignment fuel passage diameter and inlet fuel passage shaping were explored and are reported here. More PM reduction may be possible with improved geometric design and attention to alignment practices.

02 PETROLEUM↗