Container Ship Cargo Management System

Rationale: Comparing system output against class-approved hydrostatic data for 50 conditions validates both the calculation algorithms and the hydrostatic data tables. The 0.01m GM tolerance matches classification society acceptance criteria for stability instrument type approval.

Rationale: Classification society approval of loading instruments requires demonstration that shear force and bending moment calculations match approved finite element analysis within defined tolerances. The 2 percent tolerance aligns with IACS UR S11 acceptance criteria.

Rationale: Alarm system verification requires controlled injection of values at each threshold boundary to confirm correct alarm state transitions and timing. This is a standard type-approval test procedure for maritime safety systems per IEC 62923.

Rationale: NMEA signal simulation is the standard method for verifying maritime sensor interfaces without requiring physical sensors. Testing against known draught values validates the entire signal acquisition chain from connector to displayed value.

Rationale: Classification societies require independent verification of damage stability calculations against approved methods. The 0.005 tolerance on the subdivision index matches DNV GL and Lloyd Register type-approval criteria for stability instruments.

Rationale: Degraded mode testing validates that the system correctly detects sensor loss, adjusts its calculation methodology, and alerts operators. The 5 cm accuracy and 2-second indication thresholds must be verified under controlled conditions before operational approval.

Rationale: The weather criterion is the most common stability failure mode for container ships due to high windage area. Verification against approved booklet values ensures the system correctly implements the complex multi-step A.749(18) Regulation 3.2 calculation.

Rationale: The stowage planning interface is the most frequently exercised data path during cargo operations. Verification must demonstrate both throughput and reliability because a dropped or delayed stability check could allow an unsafe loading move to proceed.

Rationale: Tests the full data pipeline from Container Tracking to Stowage Planning at maximum vessel capacity (20,000 TEU). Synthetic manifest injection is chosen over live data to ensure reproducible test conditions and cover edge cases (overweight containers, special cargo types) that may not appear in operational data.

Rationale: Round-trip latency measurement validates that stability checking does not become a bottleneck in iterative stowage planning. 100 loading sequences provides statistical significance for response time characterisation across different vessel loading conditions (ballast, full, partial).

Rationale: 200 stack configurations spanning the full weight and height range exercises the lashing calculation across CSS Code force tables. Boundary conditions (maximum stack weight at maximum tier height) are the most structurally demanding cases and must be explicitly tested.

Rationale: All 9 IMDG hazard classes must be represented in the test manifest because segregation rules and stowage categories differ significantly between classes. A test covering only common classes (3, 8, 9) would miss the most restrictive segregation requirements (Class 1, Class 7).

Rationale: Tests the reefer plug availability query with a pre-configured vessel model to verify correct slot-level granularity. The mix of occupied and available slots ensures the interface correctly reports available capacity, preventing reefer over-allocation during planning.

Rationale: Tests the stowage optimization engine against the full capacity range (5,000 to 20,000 TEU) to verify the 10-minute solve time constraint holds at scale. A single compute node constraint ensures the performance target is validated without cluster parallelism masking bottlenecks.

Rationale: Known violation configurations (insufficient separation, prohibited stowage category, incompatible adjacent classes) test the negative path — the system must correctly reject every violation. This is the safety-critical test; a false negative (missed violation) could result in incompatible DG stowage at sea.

Rationale: Sequential UN number lookups across all 9 hazard classes validate both response time and classification accuracy for the synchronous query-response interface. 100 queries provides sufficient coverage of the DGL while keeping test execution time manageable.

Rationale: Batch query testing at 25, 50, and 100 UN numbers validates scalability of the database interface and verifies that batch processing does not introduce data completeness issues (missing fields, truncated results) at higher batch sizes.

Rationale: Known segregation violations with specific class combinations (1.1/5.1 incompatibility, 3/4.3 minimum separation) test that the validator correctly applies the IMDG Code Table 7.2.4 rules for the most hazardous pairings. These specific combinations are chosen because they represent the highest-consequence segregation failures.

Rationale: Tests data consistency propagation — when a DG declaration is updated, the manifest generator must reflect the change within 60 seconds. This verifies that the validated data pathway from declaration to manifest does not introduce stale data, which could result in an incorrect statutory manifest.

Rationale: 500 DG containers across all hazard classes exercises the constraint set generation at realistic vessel loads. Verification of output delivery time ensures constraint generation does not become a bottleneck during stowage planning iterations.

Rationale: Simulating 1200 containers at 15-minute intervals validates the full-scale polling load on the monitoring-to-alarm interface. Latency measurement ensures alarm processing is not delayed by interface congestion at maximum fleet size.

Rationale: 10,000 sequential records tests the guaranteed delivery mechanism of the logging interface under sustained write load. Record loss in the temperature logging chain would invalidate the tamper-evident audit trail required for regulatory compliance.

Rationale: 50 power state change events across energise, trip, and load-shed states validates the bidirectional status exchange. Timing measurement confirms that the monitoring system receives power status updates fast enough to suppress false temperature alarms during planned power transitions.

Rationale: 200 containers with a mix of pass, fail, and conditional PTI results exercises all outcome paths. Conditional results (e.g., minor defects requiring monitoring) are the most complex case and must be correctly reflected in the monitoring unit's container status.

Rationale: 50 CA reefer containers with known O2 and CO2 deviations validates that the alarm processor correctly applies atmosphere-specific thresholds. CA alarm processing is distinct from temperature alarm processing and must be tested independently to confirm correct threshold evaluation.

Rationale: 20 power fault events across different fault types (overload, ground fault, phase loss, load-shed) validates that each fault type is correctly classified and delivered to the alarm processor. Fault type identification is critical for correct crew response — a ground fault requires different action than an overload trip.

Rationale: Validates the VERMAS interface at scale to ensure the VGM system can handle peak port call volumes without message loss or parsing failures.

Rationale: Each weighbridge protocol must be independently verified since protocol-specific timing and framing issues only manifest during sustained measurement sequences.

Rationale: VGM status lifecycle transitions are critical for loading authorization decisions. Testing the full lifecycle verifies that status changes propagate correctly to the stowage planner.

Rationale: Weight lookup performance under full database load must be verified against the 100ms threshold to ensure stability calculations are not bottlenecked during planning cycles.

Rationale: Rapid VGM status updates during active cargo operations stress the synchronization path between the weight database and the container inventory, exposing race conditions.

Rationale: Dashboard accuracy and refresh rates must be validated under realistic port call loads to ensure the cargo officer has reliable situational awareness during operations.

Rationale: Discrepancy detection thresholds must be verified with known test vectors spanning all severity tiers to confirm correct classification and zero false negatives on weight safety violations.

Rationale: Full pipeline validation of 500 containers tests the compliance validator's accuracy and performance at realistic scale, covering all four authorization states and edge cases.

Rationale: The lashing calculator's dependency on accurate weight and position data from the stowage engine makes this interface safety-critical. Integration testing validates end-to-end data fidelity.

Rationale: Stability parameters directly affect lashing force calculations. Verifying that injected GM and roll period values produce correct acceleration factors validates the CSS Code calculation chain.

Rationale: BAPLIE/COPARN/CODECO are the three core message types for vessel-terminal data exchange. Integration testing validates correct parsing, routing, and semantic preservation across the full EDI stack.

Rationale: Slot availability queries occur on every optimization iteration. Performance testing at 24,000 TEU scale validates that the Bay Plan Manager meets response time requirements under realistic load.

Rationale: VGM-to-registry propagation timing is critical for preventing loading of containers with disputed weight data. Testing with discrepancy flag injection validates the full alert pipeline.

Rationale: The parser-to-queue interface handles the highest message volume in the system. 10,000 mixed-type messages tests throughput and queue acknowledgment latency under stress conditions.

Rationale: Communication link failover timing directly affects message delivery reliability. Simulating VSAT failure validates the detection-to-notification chain that triggers store-and-forward mode.

Rationale: Large BAPLIE messages (full vessel plans for 24,000 TEU) test the connector-parser interface at maximum message size, exposing buffer overflow or parsing timeout issues.

Rationale: Simultaneous alarms from all five subsystems represent the worst-case alarm flood scenario (e.g., heavy weather). Testing propagation latency validates the alarm aggregation system under peak load.

Rationale: A 50-move restow scenario against a loaded 20,000 TEU base case is the most computationally intensive simulation the system must support. This validates both calculation accuracy and response time under realistic planning conditions.

Rationale: Out-of-sequence container moves during multi-crane operations are a common operational scenario. Testing with deliberate sequence violations validates the display's ability to detect and alert on deviations from the planned loading sequence.

Rationale: The ballast interface carries safety-critical data affecting stability calculations. Testing with a 20-tank simulator covers the full range of a typical container vessel ballast system. The 5-second latency threshold ensures tank sounding updates are reflected in the stability calculation before the next crane lift cycle. Verifying the operator confirmation workflow for transmitted ballast recommendations ensures the human-in-the-loop safety control is functioning correctly.

Rationale: Wind force is the dominant environmental load on above-deck container stacks. Testing the full 0-40 m/s range covers Beaufort 0 through hurricane force winds, spanning all operational and survival conditions. The 60-second fallback verification ensures the system reverts to conservative design values promptly when actual wind data is lost, preventing lashing calculations based on stale environmental data that could under-predict forces during rapidly deteriorating weather.

Rationale: Self-test integrity is the primary defense against silent data corruption in the stability calculation. Injecting a deliberate single-bit corruption into hydrostatic tables verifies that the checksum algorithm detects the smallest possible data fault. Verifying the 24-hour periodic test ensures the system does not rely solely on startup checks, catching in-service corruption between power cycles. Alarm display verification confirms the operator is informed of integrity failures before the next stability calculation uses potentially corrupted data.